Amazon:

"DevOps is the combination of cultural philosophies, practices, and tools that increases an organization’s ability to deliver applications and services at high velocity: evolving and improving products at a faster pace than organizations using traditional software development and infrastructure management processes. This speed enables organizations to better serve their customers and compete more effectively in the market."

Microsoft:

"DevOps is the union of people, process, and products to enable continuous delivery of value to our end users. The contraction of “Dev” and “Ops” refers to replacing siloed Development and Operations to create multidisciplinary teams that now work together with shared and efficient practices and tools. Essential DevOps practices include agile planning, continuous integration, continuous delivery, and monitoring of applications."

Red Hat:

"DevOps describes approaches to speeding up the processes by which an idea (like a new software feature, a request for enhancement, or a bug fix) goes from development to deployment in a production environment where it can provide value to the user. These approaches require that development teams and operations teams communicate frequently and approach their work with empathy for their teammates. Scalability and flexible provisioning are also necessary. With DevOps, those that need power the most, get it—through self service and automation. Developers, usually coding in a standard development environment, work closely with IT operations to speed software builds, tests, and releases—without sacrificing reliability."

Google:

"...The organizational and cultural movement that aims to increase software delivery velocity, improve service reliability, and build shared ownership among software stakeholders"

• Collaboration
• Improved delivery
• Security
• Speed
• Scale
• Reliability

• One person is in charge of different tasks. For example there is only one person who is allowed to merge the code of everyone else into the repository
• Treating production differently from development environment. For example, not implementing security in development environment
• Not allowing someone to push to production on Friday ;)

• Version control is the sytem of tracking and managing changes to software code.
• It helps software teams to manage changes to source code over time.
• Version control also helps developers move faster and allows software teams to preserve efficiency and agility as the team scales to include more developers.

• In Git, a commit is a snapshot of your repo at a specific point in time.
• The git commit command will save all staged changes, along with a brief description from the user, in a “commit” to the local repository.

• Merging is Git's way of putting a forked history back together again. The git merge command lets you take the independent lines of development created by git branch and integrate them into a single branch.

• A merge conflict is an event that occurs when Git is unable to automatically resolve differences in code between two commits. When all the changes in the code occur on different lines or in different files, Git will successfully merge commits without your help.

• Use a descriptive commit message
• Make each commit a logical unit
• Incorporate others' changes frequently
• Share your changes frequently
• Coordinate with your co-workers
• Don't commit generated files

A development practice where developers integrate code into a shared repository frequently. It can range from a couple of changes every day or a week to a couple of changes in one hour in larger scales.

Each piece of code (change/patch) is verified, to make the change is safe to merge. Today, it's a common practice to test the change using an automated build that makes sure the code can integrated. It can be one build which runs several tests in different levels (unit, functional, etc.) or several separate builds that all or some has to pass in order for the change to be merged into the repository.

A development strategy used by developers to release software automatically into production where any code commit must pass through an automated testing phase. Only when this is successful is the release considered production worthy. This eliminates any human interaction and should be implemented only after production-ready pipelines have been set with real-time monitoring and reporting of deployed assets. If any issues are detected in production it should be easy to rollback to previous working state.

For more info please read here

There are many answers for such a question, as CI processes vary, depending on the technologies used and the type of the project to where the change was submitted. Such processes can include one or more of the following stages:

• Compile
• Build
• Install
• Configure
• Update
• Test

An example of one possible answer:

A developer submitted a pull request to a project. The PR (pull request) triggered two jobs (or one combined job). One job for running lint test on the change and the second job for building a package which includes the submitted change, and running multiple api/scenario tests using that package. Once all tests passed and the change was approved by a maintainer/core, it's merged/pushed to the repository. If some of the tests failed, the change will not be allowed to merged/pushed to the repository.

A complete different answer or CI process, can describe how a developer pushes code to a repository, a workflow then triggered to build a container image and push it to the registry. Once in the registry, the k8s cluster is applied with the new changes.

A development strategy used to frequently deliver code to QA and Ops for testing. This entails having a staging area that has production like features where changes can only be accepted for production after a manual review. Because of this human entanglement there is usually a time lag between release and review making it slower and error prone as compared to continous deployment.

For more info please read here

• Automated process of building, testing and deploying software
• Commit and test often
• Testing/Staging environment should be a clone of production environment

There are multiple approaches as to where to store the CI/CD pipeline definitions:

1. App Repository - store them in the same repository of the application they are building or testing (perhaps the most popular one)
2. Central Repository - store all organization's/project's CI/CD pipelines in one separate repository (perhaps the best approach when multiple teams test the same set of projects and they end up having many pipelines)
3. CI repo for every app repo - you separate CI related code from app code but you don't put everything in one place (perhaps the worst option due to the maintenance)

Both have advantages and disadvantages. With "configuration->deployment" model for example, where you build one image to be used by multiple deployments, there is less chance of deployments being different from one another, so it has a clear advantage of a consistent environment.

In your answer you can mention one or more of the following:

• mature/stable vs. cutting edge
• community size
• architecture aspects - agent vs. agentless, master vs. masterless, etc.
• learning curve

In mutable infrastructure paradigm, changes are applied on top of the existing infrastructure and over time the infrastructure builds up a history of changes. Ansible, Puppet and Chef are examples of tools which follow mutable infrastructure paradigm.

In immutable infrastructure paradigm, every change is actually a new infrastructure. So a change to a server will result in a new server instead of updating it. Terraform is an example of technology which follows the immutable infrastructure paradigm.

Read this fantastic article on the topic.

From the article: "Thus, software distribution is about the mechanism and the community that takes the burden and decisions to build an assemblage of coherent software that can be shipped."

Different distributions can focus on different things like: focus on different environments (server vs. mobile vs. desktop), support specific hardware, specialize in different domains (security, multimedia, ...), etc. Basically, different aspects of the software and what it supports, get different priority in each distribution.

Wikipedia: "A software repository, or “repo” for short, is a storage location for software packages. Often a table of contents is stored, as well as metadata."

Read more here

• Source - Maintain build script within version control system so that user can build your app after cloning repository. Advantage: User can quickly checkout different versions of application. Disadvantage: requires build tools installed on users machine.
• Archive - collect all your app files into one archive (e.g. tar) and deliver it to the user. Advantage: User gets everything he needs in one file. Disadvantage: Requires repeating the same procedure when updating, not good if there are a lot of dependencies.
• Package - depends on the OS, you can use your OS package format (e.g. in RHEL/Fefodra it's RPM) to deliver your software with a way to install, uninstall and update it using the standard packager commands. Advantages: Package manager takes care of support for installation, uninstallation, updating and dependency management. Disadvantage: Requires managing package repository.
• Images - Either VM or container images where your package is included with everything it needs in order to run successfully. Advantage: everything is preinstalled, it has high degree of environment isolation. Disadvantage: Requires knowledge of building and optimizing images.

• Cathedral - source code released when software is released
• Bazaar - source code is always available publicly (e.g. Linux Kernel)

Caching is fast access to frequently used resources which are computationally expensive or IO intensive and do not change often. There can be several layers of cache that can start from CPU caches to distributed cache systems. Common ones are in memory caching and distributed caching.
Caches are typically data structures that contains some data, such as a hashtable or dictionary. However, any data structure can provide caching capabilities, like set, sorted set, sorted dictionary etc. While, caching is used in many applications, they can create subtle bugs if not implemented correctly or used correctly. For example,cache invalidation, expiration or updating is usually quite challenging and hard.

Stateless applications don't store any data in the host which makes it ideal for horizontal scaling and microservices. Stateful applications depend on the storage to save state and data, typically databases are stateful applications.

Reliability, when used in DevOps context, is the ability of a system to recover from infrastructure failure or disruption. Part of it is also being able to scale based on your organization or team demands.

Styling, unit, functional, API, integration, smoke, scenario, ...

You should be able to explain those that you mention.

There are multiple ways to answer this question (there is no right and wrong here):

• Simple cron job
• Pipeline with configuration management technology (such Puppet, Ansible, Chef, etc.) ...

Wikipedia: "Chaos engineering is the discipline of experimenting on a software system in production in order to build confidence in the system's capability to withstand turbulent and unexpected conditions"

Read about Chaos Engineering here

Configuration drift happens when in an environment of servers with the exact same configuration and software, a certain server or servers are being applied with updates or configuration which other servers don't get and over time these servers become slightly different than all others.

This situation might lead to bugs which hard to identify and reproduce.

Declarative - You write code that specifies the desired end state Procedural - You describe the steps to get to the desired end state

Declarative Tools - Terraform, Puppet, CloudFormation Procedural Tools - Ansible, Chef

To better emphasize the difference, consider creating two virtual instances/servers. In declarative style, you would specify two servers and the tool will figure out how to reach that state. In procedural style, you need to specify the steps to reach the end state of two instances/servers - for example, create a loop and in each iteration of the loop create one instance (running the loop twice of course).

Note: cross-dependency is when you have two or more changes to separate projects and you would like to test them in mutual build instead of testing each change separately.

GitLab: "GitOps is an operational framework that takes DevOps best practices used for application development such as version control, collaboration, compliance, and CI/CD tooling, and applies them to infrastructure automation".

Read more here

Google: "One could view DevOps as a generalization of several core SRE principles to a wider range of organizations, management structures, and personnel."

Read more about it here

Google: "the SRE team is responsible for availability, latency, performance, efficiency, change management, monitoring, emergency response, and capacity planning of their services"

Read more about it here

Atlassian: "An error budget is the maximum amount of time that a technical system can fail without contractual consequences."

Read more about it here

Wrong. No system can guarantee 100% availability as no system is safe from experiencing zero downtime. Many systems and services will fall somewhere between 99% and 100% uptime (or at least this is how most systems and services should be).

Google: "Monitoring is one of the primary means by which service owners keep track of a system’s health and availability"

Read more about it here

Jenkins is an open source automation tool written in Java with plugins built for Continuous Integration purpose. Jenkins is used to build and test your software projects continuously making it easier for developers to integrate changes to the project, and making it easier for users to obtain a fresh build. It also allows you to continuously deliver your software by integrating with a large number of testing and deployment technologies.

Jenkins integrates development life-cycle processes of all kinds, including build, document, test, package, stage, deploy, static analysis and much more.

This might be considered to be an opinionated answer:

• Old fashioned dashboards with not many options to customize it
• Containers readiness (this has improved with Jenkins X)
• By itself, it doesn't have many features. On the other hand, there many plugins created by the community to expand its abilities
• Managing Jenkins and its piplines as a code can be one hell of a nightmare

You can report via:

• Emails
• Messaging apps
• Dashboards

Each has its own disadvantages and advantages. Emails for example, if sent too often, can be eventually disregarded or ignored.

The pipelines will have multiple stages:

• Clone the project
• Install test dependencies (for example, if I need tox package to run the tests, I will install it in this stage)
• Run unit tests
• (Optional) report results (For example an email to the users)
• Archive the relevant logs/files

You can describe the UI way to add new nodes but better to explain how to do in a way that scales like a script or using dynamic source for nodes like one of the existing clouds.

• Testing cross-dependencies (changes from multiple projects together)
• Starting builds from any stage (although cloudbees implemented something called checkpoints)

• Pay as you go (or consumption-based payment) - you are paying only for what you are using. No upfront payments and payment stops when resources are no longer used.
• Scalable - resources are scaled down or up based on demand

IAAS - Infrastructure as a Service PAAS - Platform as a Service SAAS - Software as a Service

• Public
• Hybrid
• Private

In cloud providers, someone else owns and manages the hardware, hire the relevant infrastructure teams and pays for real-estate (for both hardware and people). You can focus on your business.

In On-Premise solution, it's quite the opposite. You need to take care of hardware, infrastructure teams and pay for everything which can be quite expensive. On the other hand it's tailored to your needs.

The main idea behind serverless computing is that you don't need to manage the creation and configuration of server. All you need to focus on is splitting your app into multiple functions which will be triggered by some actions.

It's important to note that:

• Serverless Computing is still using servers. So saying there are no servers in serverless computing is completely wrong
• Serverless Computing allows you to have a different paying model. You basically pay only when your functions are running and not when the VM or containers are running as in other payment models

Within each region, there are multiple isolated locations known as Availability Zones. Multiple availability zones ensure high availability in case one of them goes down.

Edge locations are basically content delivery network which caches data and insures lower latency and faster delivery to the users in any location. They are located in major cities in the world.

Full explanation is here In short: it's used for managing users, groups, access policies & roles

True

• Set up MFA
• Delete root account access keys
• Create IAM users instead of using root for daily management

A way for allowing a service of AWS to use another service of AWS. You assign roles to AWS resources. For example, you can make use of a role which allows EC2 service to acesses s3 buckets (read and write).

Policies documents used to give permissions as to what a user, group or role are able to do. Their format is JSON.

There can be several reasons for that. One of them is lack of policy. To solve that, the admin has to attach the user with a policy what allows him to access the s3 bucket.

Only a login access.

"a web service that provides secure, resizable compute capacity in the cloud". Read more here

Amazon Machine Images is "An Amazon Machine Image (AMI) provides the information required to launch an instance". Read more here

• Personal AMIs - AMIs you create
• AWS Marketplace for AMIs - Paid AMIs usually with bundled with licensed software
• Community AMIs - Free

"the instance type that you specify determines the hardware of the host computer used for your instance" Read more about instance types here

False. From the above list only compute optimized is available.

"provides block level storage volumes for use with EC2 instances. EBS volumes behave like raw, unformatted block devices." More on EBS here

On Demand - pay a fixed rate by the hour/second with no commitment. You can provision and terminate it at any given time. Reserved - you get capacity reservation, basically purchase an instance for a fixed time of period. The longer, the cheaper. Spot - Enables you to bid whatever price you want for instances or pay the spot price. Dedicated Hosts - physical EC2 server dedicated for your use.

"A security group acts as a virtual firewall that controls the traffic for one or more instances" More on this subject here

EBS

Standard RI - most significant discount + suited for steady-state usage Convertible RI - discount + change attribute of RI + suited for steady-state usage Scheduled RI - launch within time windows you reserve

Learn more about EC2 RI here

AWS definition: "AWS Lambda lets you run code without provisioning or managing servers. You pay only for the compute time you consume."

Read more on it here

False. Charges are being made when the code is executed.

Amazon definition: "Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service. Customers such as Duolingo, Samsung, GE, and Cook Pad use ECS to run their most sensitive and mission critical applications because of its security, reliability, and scalability."

Learn more here

Amazon definition: "Amazon Elastic Container Registry (ECR) is a fully-managed Docker container registry that makes it easy for developers to store, manage, and deploy Docker container images."

Learn more here

Amazon definition: "AWS Fargate is a serverless compute engine for containers that works with both Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS)."

Learn more here

S3 stands for 3 S, Simple Storage Service. S3 is a object storage service which is fast, scalable and durable. S3 enables customers to upload, download or store any file or object that is up to 5 TB in size.

More on S3 here

An S3 bucket is a resource which is similar to folders in a file system and allows storing objects, which consist of data.

True

• Folder - any sub folder in an s3 bucket
• Object - The files which are stored in a bucket

Object Durability: The percent over a one-year time period that a file will not be lost Object Availability: The percent over a one-year time period that a file will be accessible

Each object has a storage class assigned to, affecting its availability and durability. This also has effect on costs. Storage classes offered today:

• Standard:

  • Used for general, all-purpose storage (mostly storage that needs to be accessed frequently)
  • The most expensive storage class
  • 11x9% durability
  • 2x9% availability
  • Default storage class

• Standard-IA (Infrequent Access)

  • Long lived, infrequently accessed data but must be available the moment it's being accessed
  • 11x9% durability
  • 99.90% availability

• One Zone-IA (Infrequent Access):

  • Long-lived, infrequently accessed, non-critical data
  • Less expensive than Standard and Standard-IA storage classes
  • 2x9% durability
  • 99.50% availability

• Intelligent-Tiering:

  • Long-lived data with changing or unknown access patterns. Basically, In this class the data automatically moves to the class most suitable for you based on usage patterns
  • Price depends on the used class
  • 11x9% durability
  • 99.90% availability

• Glacier: Archive data with retrieval time ranging from minutes to hours

• Glacier Deep Archive: Archive data that rarely, if ever, needs to be accessed with retrieval times in hours

• Both Glacier and Glacier Deep Archive are:

  • The most cheap storage classes
  • have 9x9% durability

More on storage classes here

Glacier Deep Archive

Expedited, Standard and Bulk

False. Unlimited capacity.

"AWS Storage Gateway is a hybrid cloud storage service that gives you on-premises access to virtually unlimited cloud storage". More on Storage Gateway here

Explained in detail here

Stored Volumes - Data is located at customer's data center and periodically backed up to AWS Cached Volumes - Data is stored in AWS cloud and cached at customer's data center for quick access

AWS definition: "Amazon S3 Transfer Acceleration enables fast, easy, and secure transfers of files over long distances between your client and an S3 bucket"

Learn more here

Amazon definition: "Amazon Elastic File System (Amazon EFS) provides a simple, scalable, fully managed elastic NFS file system for use with AWS Cloud services and on-premises resources."

Learn more here

"AWS Snowmobile is an Exabyte-scale data transfer service used to move extremely large amounts of data to AWS."

Learn more here

RTO - The maximum acceptable length of time that your application can be offline.

RPO - The maximum acceptable length of time during which data might be lost from your application due to an incident.

• The Cold Method - Periodically backups and sending the backups off-site
  
• Pilot Light - Data is mirrored to an environment which is always running
• Warm Standby - Running scaled down version of production environment
• Multi-site - Duplicated environment that is always running

Lowest - Multi-site Highest - The cold method

AWS definition: "Amazon CloudFront is a fast content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency, high transfer speeds, all within a developer-friendly environment."

More on CloudFront here

True

A transport solution which was designed for transferring large amounts of data (petabyte-scale) into and out the AWS cloud.

AWS definition: "Elastic Load Balancing automatically distributes incoming application traffic across multiple targets, such as Amazon EC2 instances, containers, IP addresses, and Lambda functions."

More on ELB here

AWS definition: "AWS Auto Scaling monitors your applications and automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost"

Read more about auto scaling here

False. Auto scaling adjusts capacity and this can mean removing some resources based on usage and performances.

• Application LB - layer 7 traffic
• Network LB - ultra-high performances or static IP address
• Classic LB - low costs, good for test or dev environments

The shared responsibility model defines what the customer is responsible for and what AWS is responsible for.

More on the shared responsibility model here

False. It is responsible for Hardware in its sites but not for security groups which created and managed by the users.

AWS definition: "apply to both the infrastructure layer and customer layers, but in completely separate contexts or perspectives. In a shared control, AWS provides the requirements for the infrastructure and the customer must provide their own control implementation within their use of AWS services"

Learn more about it here

AWS definition: "AWS Artifact is your go-to, central resource for compliance-related information that matters to you. It provides on-demand access to AWS’ security and compliance reports and select online agreements."

Read more about it here

AWS definition: "Amazon Inspector is an automated security assessment service that helps improve the security and compliance of applications deployed on AWS. Amazon Inspector automatically assesses applications for exposure, vulnerabilities, and deviations from best practices.""

Learn more here

AWS definition: "AWS Shield is a managed Distributed Denial of Service (DDoS) protection service that safeguards applications running on AWS."

Amazon definition: "AWS CloudHSM is a cloud-based hardware security module (HSM) that enables you to easily generate and use your own encryption keys on the AWS Cloud."

Learn more here

True

AWS definition: "KMS makes it easy for you to create and manage cryptographic keys and control their use across a wide range of AWS services and in your applications." More on KMS here

It describes prohibited uses of the web services offered by AWS. More on AWS Acceptable Use Policy here

False. On some services, like EC2, CloudFront and RDS, penetration testing is allowed.

False.

False. Security key is an example of an MFA device.

Amazon definition: "Amazon Cognito handles user authentication and authorization for your web and mobile apps."

Learn more here

Amazon definition: "AWS Certificate Manager is a service that lets you easily provision, manage, and deploy public and private Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificates for use with AWS services and your internal connected resources."

Learn more here

Amazon definition: "You can create on-demand backups of your Amazon DynamoDB tables, or you can enable continuous backups using point-in-time recovery. For more information about on-demand backups, see On-Demand Backup and Restore for DynamoDB."

Learn more here

Amazon definition: "A global table is a collection of one or more replica tables, all owned by a single AWS account."

Learn more here

Amazon definition: "Amazon DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10x performance improvement – from milliseconds to microseconds..."

Learn more here

cloud data warehouse

• You can confirm your suspicion by going to AWS Redshift console and see running queries graph. This should tell you if there are any long-running queries.
• If confirmed, you can query for running queries and cancel the irrelevant queries
• Check for connection leaks (query for running connections and include their IP)
• Check for table locks and kill irrelevant locking sessions

Amazon Elasticache is a fully managed Redis or Memcached in-memory data store. It's great for use cases like two-tier web applications where the most frequently accesses data is stored in ElastiCache so response time is optimal.

A MySQL & Postgresql based relational database. Also, the default database proposed for the user when using RDS for creating a database. Great for use cases like two-tier web applications that has a MySQL or Postgresql database layer and you need automated backups for your application.

Amazon definition: "Amazon DocumentDB (with MongoDB compatibility) is a fast, scalable, highly available, and fully managed document database service that supports MongoDB workloads. As a document database, Amazon DocumentDB makes it easy to store, query, and index JSON data."

Learn more here

EBS

AWS definition: "Amazon RDS Read Replicas provide enhanced performance and durability for RDS database (DB) instances. They make it easy to elastically scale out beyond the capacity constraints of a single DB instance for read-heavy database workloads." Read more about here

"A logically isolated section of the AWS cloud where you can launch AWS resources in a virtual network that you define" Read more about it here.

False

True. Just to clarify, a single subnet resides entirely in one AZ.

"component that allows communication between instances in your VPC and the internet" (AWS docs). Read more about it here

True

False. Only one internet gateway can be attached to a single VPC.

False.

Read more about it here and here

Allows you to connect your corporate network to AWS network.

AWS CodeDeploy

CloudFormation

Cognito

Lightsail

Cost Explorer

Trusted Advisor

AWS Snowball

VPC

Amazon Aurora

AWS Database Migration Service (DMS)

AWS CloudTrail

AWS RDS

AWS DynamoDB

AWS Rekognition

AWS X-Ray

SNS

AWS Athena

Amazon GuardDuty

AWS Organizations

AWS WAF

CloudWatch

AWS Inspector

Route 53

Amazon DocumentDB

AWS Cognito

Simple Queue Service (SQS)

AWS Shield

ElastiCache

Amazon S3 Transfer Acceleration

Route 53

"Amazon Route 53 is a highly available and scalable cloud Domain Name System (DNS) web service..." Some of Route 53 features:

• Register domain
• DNS service - domain name translations
• Health checks - verify your app is available

More on Route 53 here

AWS definition: "Amazon CloudWatch is a monitoring and observability service..."

More on CloudWatch here

AWS definition: "AWS CloudTrail is a service that enables governance, compliance, operational auditing, and risk auditing of your AWS account."

Read more on CloudTrail here

AWS definition: "a highly available, durable, secure, fully managed pub/sub messaging service that enables you to decouple microservices, distributed systems, and serverless applications."

Read more about it here

AWS definition: "AWS Organizations helps you centrally govern your environment as you grow and scale your workloads on AWS." More on Organizations here

AWS organizations service and the definition by Amazon: "SCPs offer central control over the maximum available permissions for all accounts in your organization, allowing you to ensure your accounts stay within your organization’s access control guidelines."

Learn more here

It mainly works on "pay-as-you-go" meaning you pay only for what are using and when you are using it. In s3 you pay for 1. How much data you are storing 2. Making requests (PUT, POST, ...) In EC2 it's based on the purchasing option (on-demand, spot, ...), instance type, AMI type and the region used.

More on AWS pricing model here

• TCO calculator
• AWS simple calculator
• Cost Explorer

• 24x7 customer service
• Trusted Advisor
• AWS personal Health Dashoard

Amazon definition: "Amazon Connect is an easy to use omnichannel cloud contact center that helps companies provide superior customer service at a lower cost."

Learn more here

Amazon definition: "APN Consulting Partners are professional services firms that help customers of all types and sizes design, architect, build, migrate, and manage their workloads and applications on AWS, accelerating their journey to the cloud."

Learn more here

True. You pay differently based on the chosen region.

AWS Definition: "AWS Infrastructure Event Management is a structured program available to Enterprise Support customers (and Business Support customers for an additional fee) that helps you plan for large-scale events such as product or application launches, infrastructure migrations, and marketing events."

Amazon definition: "AWS CodeDeploy is a fully managed deployment service that automates software deployments to a variety of compute services such as Amazon EC2, AWS Fargate, AWS Lambda, and your on-premises servers."

Learn more here

AWS definition: "Lightsail is an easy-to-use cloud platform that offers you everything needed to build an application or website, plus a cost-effective, monthly plan."

AWS definition: "Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning expertise to use."

Learn more here

Amazon definition: "You can use resource groups to organize your AWS resources. Resource groups make it easier to manage and automate tasks on large numbers of resources at one time. "

Learn more here

Amazon definition: "AWS Global Accelerator is a service that improves the availability and performance of your applications with local or global users..."

Learn more here

Amazon definition: "AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources."

Learn more here

AWS definition: "AWS X-Ray helps developers analyze and debug production, distributed applications, such as those built using a microservices architecture." Learn more here

Amazon definition: "AWS OpsWorks is a configuration management service that provides managed instances of Chef and Puppet."

Learn more about it here

"Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL."

Learn more about AWS Athena here

Amazon definition: "Amazon Cloud Directory is a highly available multi-tenant directory-based store in AWS. These directories scale automatically to hundreds of millions of objects as needed for applications."

Learn more here

AWS definition: "AWS Elastic Beanstalk is an easy-to-use service for deploying and scaling web applications and services...You can simply upload your code and Elastic Beanstalk automatically handles the deployment"

Learn more about it here

Amazon definition: "Amazon SWF helps developers build, run, and scale background jobs that have parallel or sequential steps. You can think of Amazon SWF as a fully-managed state tracker and task coordinator in the Cloud."

Learn more on Amazon Simple Workflow Service here

AWS definition: "big data platform for processing vast amounts of data using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto."

Learn more here

AWS definition: "Quick Starts are built by AWS solutions architects and partners to help you deploy popular technologies on AWS, based on AWS best practices for security and high availability."

Read more here

Amazon definition: "AWS Service Catalog allows organizations to create and manage catalogs of IT services that are approved for use on AWS."

Learn more here

Amazon definition: "AWS Professional Services created the AWS Cloud Adoption Framework (AWS CAF) to help organizations design and travel an accelerated path to successful cloud adoption. "

Learn more here

AWS definition: "AWS Cloud9 is a cloud-based integrated development environment (IDE) that lets you write, run, and debug your code with just a browser"

Amazon definition: "AWS Application Discovery Service helps enterprise customers plan migration projects by gathering information about their on-premises data centers."

Learn more here

AWS definition: "The Well-Architected Framework has been developed to help cloud architects build secure, high-performing, resilient, and efficient infrastructure for their applications. Based on five pillars — operational excellence, security, reliability, performance efficiency, and cost optimization"

Learn more here

AWS Lambda AWS Athena

AWS definition: "Amazon Simple Queue Service (SQS) is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications".

Learn more about it here

Ethernet simply refers to the most common type of Local Area Network (LAN) used today. A LAN—in contrast to a WAN (Wide Area Network), which spans a larger geographical area—is a connected network of computers in a small area, like your office, college campus, or even home.

A set of protocols that define how two or more devices can communicate with each other. To learn more about TCP/IP, read here

A MAC address is a unique identification number or code used to identify individual devices on the network.

Packets that are sent on the ethernet are always coming from a MAC address and sent to a MAC address. If a network adapter is receiving a packet, it is comparing the packet’s destination MAC address to the adapter’s own MAC address.

An Internet Protocol address (IP address) is a numerical label assigned to each device connected to a computer network that uses the Internet Protocol for communication.An IP address serves two main functions: host or network interface identification and location addressing.

A Subnet mask is a 32-bit number that masks an IP address, and divides the IP address into network address and host address. Subnet Mask is made by setting network bits to all "1"s and setting host bits to all "0"s. Within a given network, two host addresses are reserved for special purpose, and cannot be assigned to hosts. The "0" address is assigned a network address and "255" is assigned to a broadcast address, and they cannot be assigned to hosts.

For Example

| Address Class | No of Network Bits | No of Host Bits | Subnet mask     | CIDR notation |
| ------------- | ------------------ | --------------- | --------------- | ------------- |
| A             | 8                  | 24              | 255.0.0.0       | /8            |
| A             | 9                  | 23              | 255.128.0.0     | /9            |
| A             | 12                 | 20              | 255.240.0.0     | /12           |
| A             | 14                 | 18              | 255.252.0.0     | /14           |
| B             | 16                 | 16              | 255.255.0.0     | /16           |
| B             | 17                 | 15              | 255.255.128.0   | /17           |
| B             | 20                 | 12              | 255.255.240.0   | /20           |
| B             | 22                 | 10              | 255.255.252.0   | /22           |
| C             | 24                 | 8               | 255.255.255.0   | /24           |
| C             | 25                 | 7               | 255.255.255.128 | /25           |
| C             | 28                 | 4               | 255.255.255.240 | /28           |
| C             | 30                 | 2               | 255.255.255.252 | /30           |

You can read more about the OSI model in penguintutor.com

Unitcast: One to one communication where there is one sender and one receiver.

Broadcast: Sending a message to everyone in the network. The address ff:ff:ff:ff:ff:ff is used for broadcasting. Two common protocols which use broadcast are ARP and DHCP.

Multicast: Sending a message to a group of subscribers. It can be one-to-many or many-to-many.

CSMA/CD stands for Carrier Sense Multiple Access / Collision Detection. Its primarily focus it to manage access to shared medium/bus where only one host can transmit at a given point of time.

CSMA/CD algorithm:

1. Before sending a frame, it checks whether another host already transmitting a frame.
2. If no one transmitting, it starts transmitting the frame.
3. If two hosts transmitted at the same time, we have a collision.
4. Both hosts stop sending the frame and they send to everyone a 'jam signal' notifying everyone that a collision occurred
5. They are waiting for a random time before sending again
6. Once each host waited for a random time, they try to send the frame again and so the

A router is a physical or virtual appliance that passes information between two or more packet-switched computer networks. A router inspects a given data packet's destination Internet Protocol address (IP address), calculates the best way for it to reach its destination and then forwards it accordingly.

Network Address Translation (NAT) is a process in which one or more local IP address is translated into one or more Global IP address and vice versa in order to provide Internet access to the local hosts.

A proxy server acts as a gateway between you and the internet. It’s an intermediary server separating end users from the websites they browse.

If you’re using a proxy server, internet traffic flows through the proxy server on its way to the address you requested. The request then comes back through that same proxy server (there are exceptions to this rule), and then the proxy server forwards the data received from the website to you.

roxy servers provide varying levels of functionality, security, and privacy depending on your use case, needs, or company policy.

TCP 3-way handshake or three-way handshake is a process which is used in a TCP/IP network to make a connection between server and client.

A three-way handshake is primarily used to create a TCP socket connection. It works when:

• A client node sends a SYN data packet over an IP network to a server on the same or an external network. The objective of this packet is to ask/infer if the server is open for new connections.
• The target server must have open ports that can accept and initiate new connections. When the server receives the SYN packet from the client node, it responds and returns a confirmation receipt – the ACK packet or SYN/ACK packet.
• The client node receives the SYN/ACK from the server and responds with an ACK packet.

From wikipedia: "the length of time it takes for a signal to be sent plus the length of time it takes for an acknowledgement of that signal to be received"

Bonus question: what is the RTT of LAN?

TCP establishes a connection between the client and the server to guarantee the order of the packages, on the other hand, UDP does not establish a connection between client and server and doesn't handle package order. This makes UDP more lightweight than TCP and a perfect candidate for services like streaming.

Penguintutor.com provides a good explanation.

A default gateway serves as an access point or IP router that a networked computer uses to send information to a computer in another network or the internet.

ARP stands for Address Resolution Protocol. When you try to ping an IP address on your local network, say 192.168.1.1, your system has to turn the IP address 192.168.1.1 into a MAC address. This involves using ARP to resolve the address, hence its name.

Systems keep an ARP look-up table where they store information about what IP addresses are associated with what MAC addresses. When trying to send a packet to an IP address, the system will first consult this table to see if it already knows the MAC address. If there is a value cached, ARP is not used.

It stands for Dynamic Host Configuration Protocol, and allocates IP addresses, subnet masks and gateways to hosts. This is how it works:

• A host upon entering a network, broadcasts a message in search of a DHCP server (DHCP DISCOVER)
• An offer message is sent back by the DHCP server as a packet containing lease time, subnet mask, IP addresses, etc (DHCP OFFER)
• Depending on which offer accepted, the client sends back a reply broadcast letting all DHCP servers know (DHCP REQUEST)
• Server sends an acknowledgment (DHCP ACK)

Read more here

The exact meaning is usually depends on the context but overall data plane refers to all the functions that forward packets and/or frames from one interface to another while control plane refers to all the functions that make use of routing protocols.

There is also "Management Plane" which refers to monitoring and management functions.

Latency. To have a good latency, a search query should be forwarded to the closest datacenter.

Throughput. To have a good throughput, the upload stream should be routed to an underutilized link.

• Keep caches updated (which means the request could be forwarded not to the closest datacenter)

00110011110100011101

• Hypertext Transfer Protocol (HTTP) - used for the webpages on the internet
• Simple Mail Transfer Protocol (SMTP) - email transmission
• Telecommunications Network - (TELNET) - terminal emulation to allow client access to telnet server
• File Transfer Protocol (FTP) - facilitates transfer of files between any two machines
• Domain Name System (DNS) - domain name translation
• Dynamic Host Configuration Protocol (DHCP) - allocates IP addresses, subnet masks and gateways to hosts
• Simple Network Management Protocol (SNMP) - gathers data of devices on the network

• Internet Protocol (IP) - assists in routing packets from one machine to another
• Internet Control Message Protocol (ICMP) - lets one know what is going such as error messages and debugging information

Read more [here](https://www.globalsign.com/en/blog/what-is-hsts-and-how-do-i-use-it#:~:text=HTTP%20Strict%20Transport%20Security%20(HSTS,and%20back%20to%20the%20browser.)

Only you know :)

For example:

• Administration
• Troubleshooting & Debugging
• Storage
• Networking
• Development
• Deployments

• file permissions, number of links, owner name, owner group, file size, timestamp of last modification and directory/file name

ls -a

• cd / -> change to the root directory
• cd ~ -> change to your home directory
• cd -> change to your home directory
• cd .. -> change to the directory above your current i.e parent directory
• cd . -> change to the directory you currently in
• cd - -> change to the last visited path

Using the mv command.

• whereis
• which

Using a pipe in Linux, allows you to send the output of one to another (also called redirection). For example: cat /etc/services | wc -l

Most likely the default/generated $PATH was somehow modified or overridden thus not containing /bin/ where df would normally go. This issue could also happen if bash_profile or any configuration file of your interpreter was wrongly modified, causing erratics behaviours. You would solve this by fixing your $PATH variable:

As to fix it there are several options:

Note: There are many ways of getting errors like this: if bash_profile or any configuration file of your interpreter was wrongly modified; causing erratics behaviours, permissions issues, bad compiled software (if you compiled it by yourself)... there is no answer that will be true 100% of the time.

You can use the commands cron and at. With cron, tasks are scheduled using the following format:

*/30 * * * * bash myscript.sh Executes the script every 30 minutes.

The tasks are stored in a cron file, you can write in it using crontab -e

Alternatively if you are using a distro with systemd it's recommended to use systemd timers.

history command or .bash_history file

Using the chmod command.

• setuid is a linux file permission that permits a user to run a file or program with the permissions of the owner of that file. This is possible by elevation of current user privileges.
• setgid is a process when executed will run as the group that owns the file.

True

• No more disk space
• No more inodes
• No permissions

A daemon is a program that runs in the background without direct control of the user, although the user can at any time talk to the daemon.

systemd has many features such as user processes control/tracking, snapshot support, inhibitor locks..

If we visualize the unix/linux system in layers, systemd would fall directly after the linux kernel.

Hardware -> Kernel -> Daemons, System Libraries, Server Display.

journalctl

/var/log

tail -f <file_name>

dstat -t is great for identifying network and disk issues. netstat -tnlaup can be used to see which processes are running on which ports. lsof -i -P can be used for the same purpose as netstat. ngrep -d any metafilter for matching regex against payloads of packets. tcpdump for capturing packets wireshark same concept as tcpdump but with GUI (optional).

dstat -t is great for identifying network and disk issues. opensnoop can be used to see which files are being opened on the system (in real time).

strace is great for understanding what your program does. It prints every system call your program executed.

top will show you how much CPU percentage each process consumes perf is a great choice for sampling profiler and in general, figuring out what your CPU cycles are "wasted" on flamegraphs is great for CPU consumption visualization (http://www.brendangregg.com/flamegraphs.html)

• Check with top for anything unusual
• Run dstat -t to check if it's related to disk or network.
• Check if it's network related with sar
• Check I/O stats with iostat

The kernel is part of the operating system and is responsible for tasks like:

• Allocating memory
• Schedule processes
• Control CPU

uname -a command

The operating system executes the kernel in protected memory to prevent anyone from changing (and risking it crashing). This is what is known as "Kernel space". "User space" is where users executes their commands or applications. It's important to create this separation since we can't rely on user applications to not tamper with the kernel, causing it to crash.

Applications can access system resources and indirectly the kernel space by making what is called "system calls".

Wikipedia Definition: "SSH or Secure Shell is a cryptographic network protocol for operating network services securely over an unsecured network."

Hostinger.com Definition: "SSH, or Secure Shell, is a remote administration protocol that allows users to control and modify their remote servers over the Internet."

An SSH server will have SSH daemon running. Depends on the distribution, you should be able to check whether the service is running (e.g. systemctl status sshd).

Telnet also allows you to connect to a remote host but as opposed to SSH where the communication is encrypted, in telnet, the data is sent in clear text, so it doesn't considered to be secured because anyone on the network can see what exactly is sent, including passwords.

It means that the key of the remote host was changed and doesn't match the one that stored on the machine (in ~/.ssh/known_hosts).

1. An IP address
2. The word "error" or "failure"
3. Lines which end with a number

lines 1 and 3.

An exit code (or return code) represents the code returned by a child process to its parent process.

0 is an exit code which represents success while anything higher than 1 represents error. Each number has different meaning, based on how the application was developed.

I consider this as a good blog post to read more about it: https://shapeshed.com/unix-exit-codes

Another way to ask this: what happens from the moment you turned on the server until you get a prompt

For each file (and directory) in Linux there is an inode, a data structure which stores meta data related to the file like its size, owner, permissions, etc.

File name (it's part of the directory file)

Run mount

cat /proc/mounts

Hard link is the same file, using the same inode. Soft link is a shortcut to another file, using a different inode.

False

True

There are many answers for this question. One way is running df -T

du -sh

False. /tmp is cleared upon system boot while /var/tmp is cleared every a couple of days or not cleared at all (depends on distro).

One can use uptime or top

This article summarizes the load average topic in a great way

pidstat

iostat -xz 1

You can use the commands top and free

sar -n TCP,ETCP 1

ps -ef

You can achieve that by specifying & at the end of the command. As to why, since some commands/processes can take a lot of time to finish execution or run forever, you may want to run them in the background instead of waiting for them to finish before gaining control again in current session.

SIGTERM - default signal for terminating a process SIGHUP - common usage is for reloading configuration SIGKILL - a signal which cannot caught or ignored

To view all available signals run kill -l

A background process. Most of these processes are waiting for requests or set of conditions to be met before actually running anything. Some examples: sshd, crond, rpcbind.

A process which has finished to run but has not exited.

One reason it happens is when a parent process is programmed incorrectly. Every parent process should execute wait() to get the exit code from the child process which finished to run. But when the parent isn't checking for the child exit code, the child process can still exists although it finished to run.

You can't kill a zombie process the regular way with kill -9 for example as it's already dead.

One way to kill zombie process is by sending SIGCHLD to the parent process telling it to terminate its child processes. This might not work if the parent process wasn't programmed properly. The invocation is kill -s SIGCHLD [parent_pid]

You can also try closing/terminating the parent process. This will make the zombie process a child of init (1) which does periodic cleanups and will at some point clean up the zombie process.

If you mention at any point ps command with arugments, be familiar with what these arguments does exactly.

find /some_dir -iname *.yml -print0 | xargs -0 -r sed -i "s/1/2/g"

The ls executable is built for an incompatible architecture.

You can use the split command this way: split -l 25 some_file

In Linux (and Unix) the first three file descriptors are:

• 0 - the default data stream for input
• 1 - the default data stream for output
• 2 - the default data stream for output related to errors

This is a great article on the topic: https://www.computerhope.com/jargon/f/file-descriptor.htm

One of the following would work:

netstat -tnlp | grep <port_number>
lsof -i -n -P | grep <port_number>

False

Technically, yes.

• SSH - 22
• SMTP - 35
• HTTP - 80
• DNS - 53
• HTTPS - 443

One way would be ping6 ff02::1

There a couple of modes:

• balance-rr: round robing bonding
• active-backup: a fault tolerance mode where only one is active
• balance-tlb: Adaptive transmit load balancing
• balance-alb: Adaptive load balancing

cat /etc/hostname

You can also run hostnamectl or hostname but that might print only a temporary hostname. The one in the file is the permanent one.

You can specify one or more of the following:

• dig
• host
• nslookup

The answer depends on the distribution being used.

In Fedora/CentOS/RHEL/Rocky it can be done with rpm or dnf commands. In Ubuntu it can be done with the apt command.

Package managers allow you to manage packages lifecycle as in installing, removing and updating the packages.
In addition, you can specify in a spec how a certain package will be installed - where to copy the files, which commands to run prior to the installation, post the installation, etc.

dnf provides /usr/bin/git

Depends on the init system.

Systemd: systemctl enable [service_name] System V: update-rc.d [service_name] and add this line id:5678:respawn:/bin/sh /path/to/app to /etc/inittab Upstart: add Upstart init script at /etc/init/service.conf

1. SSH server is not installed
2. SSH server is not running

Nginx, Apache httpd.

adduser user_name --shell=/bin/false --no-create-home You can also add a user and then edit /etc/passwd.

su command. Use su - to switch to root

Re-install the OS IS NOT the right answer :)

Using the last command.

/proc/cpuinfo

dmidecoode

lsblk

ls, wc, dd, df, du, ps, ip, cp, cd ...

• touch new_file
• echo "" > new_file

$OLDPWD

• ls
• find .
• echo *

A good answer can be found here

X=2 for example. But this will persist to new shells. To have it in new shells as well, use export X=2

It's used in commands to mark the end of commands options. One common example is when used with git to discard local changes: git checkout -- some_file

From Wikipedia: "AWK is domain-specific language designed for text processing and typically used as a data extraction and reporting tool"

awk '{print $4}' file

awk 'length($0) > 79' file

fork() is used for creating a new process. It does so by cloning the calling process but the child process has its own PID and any memory locks, I/O operations and semaphores are not inherited.

wait() is used by a parent process to wait for the child process to finish execution. If wait is not used by a parent process then a child process might become a zombie process.

Executes a program. The program is passed as a filename (or path) and must be a binary executable or a script.

Unix pipe implementation

"Pipes provide a unidirectional interprocess communication channel. A pipe has a read end and a write end. Data written to the write end of a pipe can be read from the read end of the pipe. A pipe is created using pipe(2), which returns two file descriptors, one referring to the read end of the pipe, the other referring to the write end."

Notes:

• getline() originates in GNU C library and used to read lines from input stream and stores those lines in the buffer

There are a couple of ways to do that:

• dd if=/dev/urandom of=new_file.txt bs=2MB count=1
• truncate -s 2M new_file.txt
• fallocate -l 2097152 new_file.txt

open("/my/file") = 5
read(5, "file content")

These system calls are reading the file /my/file and 5 is the file descriptor number.

From wikipedia: a context switch is the process of storing the state of a process or thread, so that it can be restored and resume execution at a later point

Another common way to task this questions is "what part of the tcp header does traceroute modify?"

This is a good article about the topic: https://ops.tips/blog/how-linux-creates-sockets

MemFree - The amount of unused physical RAM in your system MemAvailable - The amount of available memory for new workloads (without pushing system to use swap) based on MemFree, Active(file), Inactive(file), and SReclaimable.

• Kernel
• Utilities
• Services
• Software/Packages Management

/dev

There are many ways to answer that. For those who look for simplicity, the book "Operating Systems: Three Easy Pieces" offers nice version:

"responsible for making it easy to run programs (even allowing you to seemingly run many at the same time), allowing programs to share memory, enabling programs to interact with devices, and other fun stuff like that"

The kernel is part of the operating system and is responsible for tasks like:

• Allocating memory
• Schedule processes
• Control CPU

True

A process is a running program. A program is one or more instructions and the program (or process) is executed by the operating system.

It would support the following:

• Create - allow to create new processes
• Delete - allow to remove/destroy processes
• State - allow to check the state of the process, whether it's running, stopped, waiting, etc.
• Stop - allow to stop a running process

Note: The loading of the program's code into the memory done lazily which means the OS loads only partial relevant pieces required for the process to run and not the entire code.

False. It was true in the past but today's operating systems perform lazy loading which means only the relevant pieces required for the process to run are loaded first.

• Running - it's executing instructions
• Ready - it's ready to run but for different reasons it's on hold
• Blocked - it's waiting for some operation to complete. For example I/O disk request

Buffer: Reserved place in RAM which is used to hold data for temporary purposes Cache: Cache is usually used when processes reading and writing to the disk to make the process faster by making similar data used by different programs easily accessible.

Red Hat: "A hypervisor is software that creates and runs virtual machines (VMs). A hypervisor, sometimes called a virtual machine monitor (VMM), isolates the hypervisor operating system and resources from the virtual machines and enables the creation and management of those VMs."

Read more here

Hosted hypervisors and bare-metal hypervisors.

Due to having its own drivers and a direct access to hardware components, a baremetal hypervisor will often have better performances along with stability and scalability.

On the other hand, there will probably be some limitation regarding loading (any) drivers so a hosted hypervisor will usually benefit from having a better hardware compatibility.

Operating system virtualization Network functions virtualization Desktop virtualization

Yes, it's a operating-system-level virtualization, where the kernel is shared and allows to use multiple isolated user-spaces instances.

Even when using a system with one physical CPU, it's possible to allow multiple users to work on it and run programs. This is possible with time sharing where computing resources are shared in a way it seems to the user the system has multiple CPUs but in fact it's simply one CPU shared by applying multiprogramming and multi-tasking.

Somewhat the opposite of time sharing. While in time sharing a resource is used for a while by one entity and then the same resource can be used by another resource, in space sharing the space is shared by multiple entities but in a way it's not being transfered between them.
It's used by one entity until this entity decides to get rid of it. Take for example storage. In storage, a file is your until you decide to delete it.

Task – a call to a specific Ansible module Module – the actual unit of code executed by Ansible on your own host or a remote host. Modules are indexed by category (database, file, network, …) and also referred to as task plugins.

Play – One or more tasks executed on a given host(s)

Playbook – One or more plays. Each play can be executed on the same or different hosts

Role – Ansible roles allows you to group resources based on certain functionality/service such that they can be easily reused. In a role, you have directories for variables, defaults, files, templates, handlers, tasks, and metadata. You can then use the role by simply specifying it in your playbook.

Ansible is:

• Agentless
• Minimal run requirements (Python & SSH) and simple to use
• Default mode is "push" (it supports also pull)
• Focus on simpleness and ease-of-use

True.

While it's possible to provision resources with Ansible, some prefer to use tools that follow immutable infrastructure paradigm. Ansible doesn't saves state by default. So a task that creates 5 instances for example, when executed again will create additional 5 instances (unless additional check is implemented) while other tools will check if 5 instances exist. If only 4 exist, additional instance will be created.

An inventory file defines hosts and/or groups of hosts on which Ansible tasks executed upon.

An example of inventory file:

192.168.1.2
192.168.1.3
192.168.1.4

[web_servers]
190.40.2.20
190.40.2.21
190.40.2.22

A dynamic inventory file tracks hosts from one or more sources like cloud providers and CMDB systems.

You should use one when using external sources and especially when the hosts in your environment are being automatically
spun up and shut down, without you tracking every change in these sources.

1. Ansible online docs
2. ansible-doc -l for list of modules and ansible [module_name] for detailed information on a specific module

---
- name: Print information about my host
  hosts: localhost
  gather_facts: 'no'
  tasks:
      - name: Print hostname
        debug:
            msg: "It's me, {{ ansible_hostname }}"

When given a written code, always inspect it thoroughly. If your answer is “this will fail” then you are right. We are using a fact (ansible_hostname), which is a gathered piece of information from the host we are running on. But in this case, we disabled facts gathering (gather_facts: no) so the variable would be undefined which will result in failure.

I'm <HOSTNAME> and my operating system is <OS>

Replace and with the actual data for the specific host you are running on

The playbook to deploy the system_info file

---
- name: Deploy /tmp/system_info file
  hosts: all:!controllers
  tasks:
      - name: Deploy /tmp/system_info
        template:
            src: system_info.j2
            dest: /tmp/system_info

The content of the system_info.j2 template

# {{ ansible_managed }}
I'm {{ ansible_hostname }} and my operating system is {{ ansible_distribution }

The right answer is ‘toad’.

Variable precedence is about how variables override each other when they set in different locations. If you didn’t experience it so far I’m sure at some point you will, which makes it a useful topic to be aware of.

In the context of our question, the order will be extra vars (always override any other variable) -> host facts -> inventory variables -> role defaults (the weakest).

Here is the order of precedence from least to greatest (the last listed variables winning prioritization):

1. command line values (eg “-u user”)
2. role defaults [1]
3. inventory file or script group vars [2]
4. inventory group_vars/all [3]
5. playbook group_vars/all [3]
6. inventory group_vars/* [3]
7. playbook group_vars/* [3]
8. inventory file or script host vars [2]
9. inventory host_vars/* [3]
10. playbook host_vars/* [3]
11. host facts / cached set_facts [4]
12. play vars
13. play vars_prompt
14. play vars_files
15. role vars (defined in role/vars/main.yml)
16. block vars (only for tasks in block)
17. task vars (only for the task)
18. include_vars
19. set_facts / registered vars
20. role (and include_role) params
21. include params
22. extra vars (always win precedence)

A full list can be found at PlayBook Variables . Also, note there is a significant difference between Ansible 1.x and 2.x.

Serial is like running the playbook for each host in turn, waiting for completion of the complete playbook before moving on to the next host. forks=1 means run the first task in a play on one host before running the same task on the next host, so the first task will be run for each host before the next task is touched. Default fork is 5 in ansible.

[defaults]
forks = 30

- hosts: webservers
  serial: 1
  tasks:
    - name: ...

Ansible also supports throttle This keyword limits the number of workers up to the maximum set via the forks setting or serial. This can be useful in restricting tasks that may be CPU-intensive or interact with a rate-limiting API

tasks:
- command: /path/to/cpu_intensive_command
  throttle: 1

Read here

• fully automated process of provisioning, modifying and deleting your infrastructure
• version control for your infrastructure which allows you to quickly rollback to previous versions
• validate infrastructure quality and stability with automated tests and code reviews
• makes infrastructure tasks less repetitive

A common wrong answer is to say that Ansible and Puppet are configuration management tools and Terraform is a provisioning tool. While technically true, it doesn't mean Ansible and Puppet can't be used for provisioning infrastructure. Also, it doesn't explain why Terraform should be used over CloudFormation if at all.

The benefits of Terraform over the other tools:

• It follows the immutable infrastructure approach which has benefits like avoiding a configuration drift over time
• Ansible and Puppet are more procedural (you mention what to execute in each step) and Terraform is declarative since you describe the overall desired state and not per resource or task. You can give the example of going from 1 to 2 servers in each tool. In Terraform you specify 2, in Ansible and puppet you have to only provision 1 additional server so you need to explicitly make sure you provision only another one server.

False. Terraform follows immutable infrastructure paradigm.

It keeps track of the IDs of created resources so that Terraform knows what it is managing.

terraform state mv

terraform init scans your code to figure which providers are you using and download them. terraform plan will let you see what terraform is about to do before actually doing it. terraform validate checks if configuration is syntactically valid and internally consistent within a directory. terraform apply will provision the resources specified in the .tf files.

You use it this way: variable “my_var” {}

It's a resource which was successfully created but failed during provisioning. Terraform will fail and mark this resource as "tainted".

string number bool list() set() map() object({<ATTR_NAME> = , ... }) tuple([, ...])

There are quite a few cases you might need to use them:

• you want to reference resources not managed through terraform
• you want to reference resources managed by a different terraform module
• you want to cleanly compute a value with typechecking, such as with aws_iam_policy_document

The primary difference between containers and VMs is that containers allow you to virtualize multiple workloads on the operating system while in the case of VMs the hardware is being virtualized to run multiple machines each with its own OS. You can also think about it as containers are for OS-level virtualization while VMs are for hardware virtualization.

• Containers don't require an entire guest operating system as VMs. Containers share the system's kernel as opposed to VMs
• It usually takes a few seconds to set up a container as opposed to VMs which can take minutes or at least more time than containers as there is an entire OS to boot and initialize as opposed to container where you mainly lunch the app itself
• Containers are isolated from each other, but not as concretely as virtual machines. It is possible for a malicious user to break into the host OS from a container and vice versa.

You should choose VMs when:

• you need run an application which requires all the resources and functionalities of an OS
• you need full isolation and security

You should choose containers when:

• you need a lightweight solution
• Running multiple versions or instances of a single application

Docker CLI passes your request to Docker daemon. Docker daemon downloads the image from Docker Hub Docker daemon creates a new container by using the image it downloaded Docker daemon redirects output from container to Docker CLI which redirects it to the standard output

dockerd - The Docker daemon itself. The highest level component in your list and also the only 'Docker' product listed. Provides all the nice UX features of Docker.

(docker-)containerd - Also a daemon, listening on a Unix socket, exposes gRPC endpoints. Handles all the low-level container management tasks, storage, image distribution, network attachment, etc...

(docker-)containerd-ctr - A lightweight CLI to directly communicate with containerd. Think of it as how 'docker' is to 'dockerd'.

(docker-)runc - A lightweight binary for actually running containers. Deals with the low-level interfacing with Linux capabilities like cgroups, namespaces, etc...

(docker-)containerd-shim - After runC actually runs the container, it exits (allowing us to not have any long-running processes responsible for our containers). The shim is the component which sits between containerd and runc to facilitate this.

In short:

Cgroups = limits how much you can use; namespaces = limits what you can see (and therefore use)

Cgroups involve resource metering and limiting: memory CPU block I/O network

Namespaces provide processes with their own view of the system

Multiple namespaces: pid,net, mnt, uts, ipc, user

docker.io/library/busybox:latest resolved to a manifestList object with 9 entries; looking for a unknown/amd64 match

found match for linux/amd64 with media type application/vnd.docker.distribution.manifest.v2+json, digest sha256:400ee2ed939df769d4681023810d2e4fb9479b8401d97003c710d0e20f7c49c6

pulling blob "sha256:61c5ed1cbdf8e801f3b73d906c61261ad916b2532d6756e7c4fbcacb975299fb Downloaded 61c5ed1cbdf8 to tempfile /var/lib/docker/tmp/GetImageBlob909736690

Applying tar in /var/lib/docker/overlay2/507df36fe373108f19df4b22a07d10de7800f33c9613acb139827ba2645444f7/diff" storage-driver=overlay2

Applied tar sha256:514c3a3e64d4ebf15f482c9e8909d130bcd53bcc452f0225b0a04744de7b8c43 to 507df36fe373108f19df4b22a07d10de7800f33c9613acb139827ba2645444f7, size: 1223534

podman run or docker run

Create a new image from a container’s changes

1. To remove one or more Docker images use the docker container rm command followed by the ID of the containers you want to remove.
2. The docker system prune command will remove all stopped containers, all dangling images, and all unused networks
3. docker rm $(docker ps -a -q) - This command will delete all stopped containers. The command docker ps -a -q will return all existing container IDs and pass them to the rm command which will delete them. Any running containers will not be deleted.

Docker can build images automatically by reading the instructions from a Dockerfile. A Dockerfile is a text document that contains all the commands a user could call on the command line to assemble an image.

COPY takes in a src and destination. It only lets you copy in a local file or directory from your host (the machine building the Docker image) into the Docker image itself. ADD lets you do that too, but it also supports 2 other sources. First, you can use a URL instead of a local file / directory. Secondly, you can extract a tar file from the source directly into the destination. Although ADD and COPY are functionally similar, generally speaking, COPY is preferred. That’s because it’s more transparent than ADD. COPY only supports the basic copying of local files into the container, while ADD has some features (like local-only tar extraction and remote URL support) that are not immediately obvious.

RUN lets you execute commands inside of your Docker image. These commands get executed once at build time and get written into your Docker image as a new layer. CMD is the command the container executes by default when you launch the built image. A Dockerfile can only have one CMD. You could say that CMD is a Docker run-time operation, meaning it’s not something that gets executed at build time. It happens when you run an image. A running image is called a container.

A common answer to this is to use hadolint project which is a linter based on Dockerfile best practices.

Compose is a tool for defining and running multi-container Docker applications. With Compose, you use a YAML file to configure your application’s services. Then, with a single command, you create and start all the services from your configuration.

For example, you can use it to set up ELK stack where the services are: elasticsearch, logstash and kibana. Each running in its own container.

• Define the services you would like to run together in a docker-compose.yml file
• Run docker-compose up to run the services

Docker Hub is a native Docker registry service which allows you to run pull and push commands to install and deploy Docker images from the Docker Hub.

Docker Cloud is built on top of the Docker Hub so Docker Cloud provides you with more options/features compared to Docker Hub. One example is Swarm management which means you can create new swarms in Docker Cloud.

A Docker image is built up from a series of layers. Each layer represents an instruction in the image’s Dockerfile. Each layer except the very last one is read-only. Each layer is only a set of differences from the layer before it. The layers are stacked on top of each other. When you create a new container, you add a new writable layer on top of the underlying layers. This layer is often called the “container layer”. All changes made to the running container, such as writing new files, modifying existing files, and deleting files, are written to this thin writable container layer. The major difference between a container and an image is the top writable layer. All writes to the container that add new or modify existing data are stored in this writable layer. When the container is deleted, the writable layer is also deleted. The underlying image remains unchanged. Because each container has its own writable container layer, and all changes are stored in this container layer, multiple containers can share access to the same underlying image and yet have their own data state.

Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications.

To understand what Kubernetes is good for, let's look at some examples:

• You would like to run a certain application in a container on multiple different locations. Sure, if it's 2-3 servers/locations, you can do it by yourself but it can be challenging to scale it up to additional multiple location.
  
• Performing updates and changes across hundreds of containers
  
• Handle cases where the current load requires to scale up (or down)

Red Hat Definition: "A Kubernetes cluster is a set of node machines for running containerized applications. If you’re running Kubernetes, you’re running a cluster.

At a minimum, a cluster contains a worker node and a master node."

Read more here

A node is a virtual machine or a physical server that serves as a worker for running the applications. It's recommended to have at least 3 nodes in Kubernetes production environment.

The master coordinates all the workflows in the cluster:

• Scheduling applications
• Managing desired state
• Rolling out new updates

The workers are the nodes which run the applications and workloads.

kubectl get nodes

False. A Kubernetes cluster consists of at least 1 master and can have 0 workers (although that wouldn't be very useful...)

• API Server - the Kubernetes API. All cluster components communicate through it
• Scheduler - assigns an application with a worker node it can run on
• Controller Manager - cluster maintenance (replications, node failures, etc.)
• etcd - stores cluster configuration

• Kubelet - an agent responsible for node communication with the master.
• Kube-proxy - load balancing traffic between app components
• Container runtime - the engine runs the containers (Podman, Docker, ...)

kubectl run my-pod --image=nginx:alpine --restart=Never

Multiple containers but in most cases it would be one container per pod.

It means they would eventually die and pods are unable to heal so it is recommended that you don't create them directly.

kubectl get pods --all-namespaces

kubectl delete pod pod_name

kubectl edit deployment some-deployment

The pod will terminate and another, new pod, will be created.

Also, when looking at the replicaset, you'll see the old replica doesn't have any pods and a new replicaset is created.

One way is by specifying the deployment name: kubectl delete deployment [deployment_name] Another way is using the deployment configuration file: kubectl delete -f deployment.yaml

The pod related to the deployment will terminate and the replicaset will be removed.

Using a Service.

In simpler words, it allows you to expose the service by attaching permanent IP address for example to a certain pod.

True

More on this topic here

kubctl describe service [service_name]

Run kubectl describe service and if the IPs from "Endpoints" match any IPs from the output of kubectl get pod -o wide

spec:
  selector:
    app: some-app
  ports:
    - protocol: TCP
      port: 8081
      targetPort: 8081

Adding type: LoadBalancer and nodePort

spec:
  selector:
    app: some-app
  type: LoadBalancer
  ports:
    - protocol: TCP
      port: 8081
      targetPort: 8081
      nodePort: 32412

Ingress

From Kubernetes docs: "Ingress exposes HTTP and HTTPS routes from outside the cluster to services within the cluster. Traffic routing is controlled by rules defined on the Ingress resource."

Read more here

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: someapp-ingress
spec:
  rules:
  - host: my.host
    http:
      paths:
      - backend:
          serviceName: someapp-internal-service
          servicePort: 8080

host is the entry point of the cluster so basically a valid domain address that maps to cluster's node IP address
the http line used for specifying that incoming requests will be forwarded to the internal service using http.
backend is referencing the internal service (serviceName is the name under metadata and servicePort is the port under the ports section).

An implementation for Ingress. It's basically another pod (or set of pods) that does evaluates and processes Ingress rules and this it manages all the redirections.

There are multiple Ingress Controller implementations (the one from Kubernetes is Kubernetes Nginx Ingress Controller).

• Multiple sub-domains (multiple host entries, each with its own service)
• One domain with multiple services (multiple paths where each one is mapped to a different service/application)

kubectl get ingress

It specifies what do with an incoming request to the Kubernetes cluster that isn't mapped to any backend (= no rule to for mapping the request to a service). If the default backend service isn't defined, it's recommended to define so users still see some kind of message instead of nothing or unclear error.

Create Service resource that specifies the name of the default backend as reflected in kubectl desrcibe ingress ... and the port under the ports section.

Add tls and secretName entries.

spec:
  tls:
  - hosts:
    - some_app.com
    secretName: someapp-secret-tls

True

It has three main parts:

1. Metadata
2. Specification
3. Status (this automatically generated and added by Kubernetes)

YAML

kubectl get deployment [deployment_name] -o yaml

etcd

True

True

True

Namespaces allow you split your cluster into virtual clusters where you can group your applications in a way that makes sense and is completely separated from the other groups (so you can for example create an app with the same name in two different namespaces)

When using the default namespace alone, it becomes hard over time to get an overview of all the applications you manage in your cluster. Namespaces make it easier to organize the applications into groups that makes sense, like a namespace of all the monitoring applications and a namespace for all the security applications, etc.

Namespaces can also be useful for managing Blue/Green environments where each namespace can include a different version of an app and also share resources that are in other namespaces (namespaces like logging, monitoring, etc.).

Another use case for namespaces is one cluster, multiple teams. When multiple teams use the same cluster, they might end up stepping on each others toes. For example if they end up creating an app with the same name it means one of the teams overriden the app of the other team because there can't be too apps in Kubernetes with the same name (in the same namespace).

False. When a namespace is deleted, the resources in that namespace are deleted as well.

• default
• kube-system
• kube-public
• kube-node-lease

• Master and Kubectl processes
• System processes

kubectl get namespaces

• A configmap, which contains cluster information
• Publicely accessible data

kubectl config view | grep namespace

It holds information on hearbeats of nodes. Each node gets an object which holds information about its availability.

One way is by running kubectl create namespace [NAMESPACE_NAME]

Another way is by using namespace configuration file:

apiVersion: v1
kind: ConfigMap
metadata:
  name: some-cofngimap
  namespace: some-namespace

Any resource you create while using Kubernetes.

True. With namespaces you can limit CPU, RAM and storage usage.

kubectl config set-context --current --namespace=some-namespace and validate with kubectl config view --minify | grep namespace:

OR

kubens some-namespace

kubectl create quota some-quota --hard-cpu=2,pods=2

Service.

No, you would have to create separate namespace in y namespace.

apiVersion: v1
kind: ConfigMap
metadata:
  name: some-configmap
data:
  some_url: samurai.jack

It's referencing the service "samurai" in the namespace called "jack".

Volume and Node.

kubectl api-resources --namespaced=true

One way is by specifying --namespace like this: kubectl apply -f my_component.yaml --namespace=some-namespace Another way is by specifying it in the YAML itself:

apiVersion: v1
kind: ConfigMap
metadata:
  name: some-configmap
  namespace: some-namespace

and you can verify with: kubectl get configmap -n some-namespace

kubectl get all | grep [APP_NAME]

Lists the components that doesn't bound to a namespace.

kubectl describe pod pod_name

kubectl exec some-pod -it -- ls

kubectl expose deploy some-deployment --port=80 --target-port=8080

kubectl run nginx --image=nginx --restart=Never --port 80 --expose

kubectl scale deploy some-deployment --replicas=8

kubectl api-resources --namespaced=false

kubectl delete pods --field-selector=status.phase!='Running'

kubectl top pod

Outputs the status of each of the control plane components.

Minikube is a lightweight Kubernetes implementation. It create a local virtual machine and deploys a simple (single node) cluster.

Start by inspecting the pods status. we can use the command kubectl get pods (--all-namespaces for pods in system namespace)

If we see "Error" status, we can keep debugging by running the command kubectl describe pod [name]. In case we still don't see anything useful we can try stern for log tailing.

In case we find out there was a temporary issue with the pod or the system, we can try restarting the pod with the following kubectl scale deployment [name] --replicas=0

Setting the replicas to 0 will shut down the process. Now start it with kubectl scale deployment [name] --replicas=1

They become candidates to for termination.

False. CPU is a compressible resource while memory is a non compressible resource - once a container reached the memory limit, it will be terminated.

Explained here

Explained here

"Operators are software extensions to Kubernetes that make use of custom resources to manage applications and their components. Operators follow Kubernetes principles, notably the control loop."

The process of managing stateful applications in Kubernetes isn't as straightforward as managing stateless applications where reaching the desired status and upgrades are both handled the same way for every replica. In stateful applications, upgrading each replica might require different handling due to the stateful nature of the app, each replica might be in a different status. As a result, we often need a human operator to manage stateful applications. Kubernetes Operator is suppose to assist with this.

This also help with automating a standard process on multiple Kubernetes clusters

1. CRD (custom resource definition)
2. Controller - Custom control loop which runs against the CRD

It uses the control loop used by Kubernetes in general. It watches for changes in the application state. The difference is that is uses a custom control loop. In additions.

In addition, it also makes use of CRD's (Custom Resources Definitions) so basically it extends Kubernetes API.

True

open source toolkit used to manage k8s native applications, called operators, in an automated and efficient way.

1. Operator SDK - allows developers to build operators
2. Operator Lifecycle Manager - helps to install, update and generally manage the lifecycle of all operators
3. Operator Metering - Enables usage reporting for operators that provide specialized services

It's part of the Operator Framework, used for managing the lifecycle of operators. It basically extends Kubernetes so a user can use a declarative way to manage operators (installation, upgrade, ...).

It includes:

• catalog-operator - Resolving and installing ClusterServiceVersions the resource they specify.
• olm-operator - Deploys applications defined by ClusterServiceVersion resource

Secrets let you store and manage sensitive information (passwords, ssh keys, etc.)

kubectl create secret generic some-secret --from-literal=password='donttellmypassword'

kubectl create secret generic some-secret --from-file=/some/file.txt

Opaque is the default type used for key-value pairs.

False. Some known security mechanisms like "encryption" aren't enabled by default.

kubectl apply -f some-secret.yaml

False

Persistent Volumes allow us to save data so basically they provide storage that doesn't depend on the pod lifecycle.

True

• NFS
• iSCSI
• CephFS
• ...

False

• Retain
• Recycle
• Delete

Namespaces will allow to limit resources and also make sure there are no collisions between teams when working in the cluster (like creating an app with the same name).

Separate configuration from pods. It's good for cases where you might need to change configuration at some point but you don't want to restart the application or rebuild the image so you create a ConfigMap and connect it to a pod but externally to the pod.

Overall it's good for:

• Sharing the same configuration between different pods
• Storing external to the pod configuration

1. Create it (from key&value, a file or an env file)
2. Attach it. Mount a configmap as a volume

False. Use secret.

Scale the number of pods automatically on observed CPU utilization.

Use "--grace-period=0 --force"

The control plane component kube-scheduler asks the following questions,

1. What to schedule? It tries to understand the pod-definition specifications
2. Which node to schedule? It tries to determine the best node with available resources to spin a pod
3. Binds the Pod to a given node

View more here

• Guaranteed
• Burstable
• BestEffort

Package manager for Kubernetes. Basically the ability to package YAML files and distribute them to other users.

Sometimes when you would like to deploy a certain application to your cluster, you need to create multiple YAML files / Components like: Secret, Service, ConfigMap, etc. This can be tedious task. So it would make sense to ease the process by introducing something that will allow us to share these bundle of YAMLs every time we would like to add an application to our cluster. This something is called Helm.

Helm Charts is a bundle of YAML files. A bundle that you can consume from repositories or create your own and publish it to the repositories.

It is useful for scenarios where you have multiple applications and all are similar, so there are minor differences in their configuration files and most values are the same. With Helm you can define a common blueprint for all of them and the values that are not fixed and change can be placeholders. This is called a template file and it looks similar to the following

apiVersion: v1
kind: Pod
metadata:
  name: {[ .Values.name ]}
spec:
  containers:
  - name: {{ .Values.container.name }}
  image: {{ .Values.container.image }}
  port: {{ .Values.container.port }}

The values themselves will in separate file:

name: some-app
container:
  name: some-app-container
  image: some-app-image
  port: 1991

• Deploy the same application across multiple different environments
• CI/CD

someChart/ -> the name of the chart Chart.yaml -> meta information on the chart values.yaml -> values for template files charts/ -> chart dependencies templates/ -> templates files :)

helm search hub [some_keyword]

Or directly on the command line: helm install --set some_key=some_value

Helm allows you to upgrade, remove and rollback to previous versions of charts. In version 2 of Helm it was with what is known as "Tiller". In version 3, it was removed due to security concerns.

"Submariner enables direct networking between pods and services in different Kubernetes clusters, either on premise or in the cloud."

You can learn more here

In static typed languages the variable type is known at compile-time instead of at run-time. Such languages are: C, C++ and Java

An expression is anything that results in a value (even if the value is None). Basically, any sequence of literals so, you can say that a string, integer, list, ... are all expressions.

Statements are instructions executed by the interpreter like variable assignments, for loops and conditionals (if-else).

SOLID design principles are about:

• Make it easier to extend the functionality of the system
• Make the code more readable and easier to maintain

SOLID is:

• Single Responsibility - A class should only have a single responsibility
• Open-Closed - An entity should be open for extension, but closed for modification. What this practically means is that you should extend functionality by adding a new code and not by modifying it. Your system should be separated into components so it can be easily extended without breaking everything.
• Liskov Substitution - Any derived class should be able to substitute the its parent without altering its corrections. Practically, every part of the code will get the expected result no matter which part is using it
• Interface segregation - A client should never depend on anything it doesn't uses
• Dependency Inversion - High level modules should depend on abstractions, not low level modules

True

It's a search algorithm used with sorted arrays/lists to find a target value by dividing the array each iteration and comparing the middle value to the target value. If the middle value is smaller than target value, then the target value is searched in the right part of the divided array, else in the left side. This continues until the value is found (or the array divided max times)

python implementation

The average performance of the above algorithm is O(log n). Best performance can be O(1) and worst O(log n).

• Stack
• Queue
• Linked List
• Binary Search Tree

• A linked list is a data structure
• It consists of a collection of nodes. Together these nodes represent a sequence
• Useful for use cases where you need to insert or remove an element from any position of the linked list
• Some programming languages don't have linked lists as a built-in data type (like Python for example) but it can be easily implemented

There are multiple ways to detect a loop in a linked list. I'll mention three here:

Worst solution:
Two pointers where one points to the head and one points to the last node. Each time you advance the last pointer by one and check whether the distance between head pointer to the moved pointer is bigger than the last time you measured the same distance (if not, you have a loop).
The reason it's probably the worst solution, is because time complexity here is O(n^2)

Decent solution:

Create an hash table and start traversing the linked list. Every time you move, check whether the node you moved to is in the hash table. If it isn't, insert it to the hash table. If you do find at any point the node in the hash table, it means you have a loop. When you reach None/Null, it's the end and you can return "no loop" value. This one is very easy to implement (just create a hash table, update it and check whether the node is in the hash table every time you move to the next node) but since the auxiliary space is O(n) because you create a hash table then, it's not the best solution

Good solution:
Instead of creating a hash table to document which nodes in the linked list you have visited, as in the previous solution, you can modify the Linked List (or the Node to be precise) to have a "visited" attribute. Every time you visit a node, you set "visited" to True.
Time compleixty is O(n) and Auxiliary space is O(1), so it's a good solution but the only problem, is that you have to modify the Linked List.

Best solution:
You set two pointers to traverse the linked list from the beginning. You move one pointer by one each time and the other pointer by two. If at any point they meet, you have a loop. This solution is also called "Floyd's Cycle-Finding"
Time complexity is O(n) and auxiliary space is O(1). Perfect :)

Mutability determines whether you can modify an object of specific type.

The mutable data types are:

List
Dictionary
Set

The immutable data types are:

Numbers (int, float, ...)
String
Bool
Tuple
Frozenset

A tuple is a built-in data type in Python. It's used for storing multiple items in a single variable.

List, as opposed to a tuple, is a mutable data type. It means we can modify it and at items to it.

bool("") -> evaluates to False
bool(" ") -> evaluates to True

It evaluates to True.
The reason is that the two created empty list are different objects. x is y only evaluates to true when x and y are the same object.

char = input("Insert a character: ")
if char == "a" or char == "o" or char == "e" or char =="u" or char == "i":
    print("It's a vowel!")

char = input("Insert a character: ") # For readablity
if lower(char[0]) in "aieou": # Takes care of multiple characters and separate cases
    print("It's a vowel!")

OR

if lower(input("Insert a character: ")[0]) in "aieou": # Takes care of multiple characters and small/Capital cases
    print("It's a vowel!")

In general, first class objects in programming languages are objects which can be assigned to variable, used as a return value and can be used as arguments or parameters.
In python you can treat functions this way. Let's say we have the following function

def my_function():
    return 5

You can then assign a function to a variables like this x = my_function or you can return functions as return values like this return my_function

In the following block of code x is a class attribute while self.y is a instance attribute

class MyClass(object):
    x = 1

    def __init__(self, y):
        self.y = y

Exceptions: Errors detected during execution are called Exceptions.

Handling Exception: When an error occurs, or exception as we call it, Python will normally stop and generate an error message.
Exceptions can be handled using try and except statement in python.

Example: Following example asks the user for input until a valid integer has been entered.
If user enter a non-integer value it will raise exception and using except it will catch that exception and ask the user to enter valid integer again.

while True:
    try:
        a = int(input("please enter an integer value: "))
        break
    except ValueError:
        print("Ops! Please enter a valid integer value.")

For more details about errors and exceptions follow this https://docs.python.org/3/tutorial/errors.html

1. Translation lookup in i18n
2. Hold the result of the last executed expression or statement in the interactive interpreter.
3. As a general purpose "throwaway" variable name. For example: x, y, _ = get_data() (x and y are used but since we don't care about third variable, we "threw it away").

A lambda expression is an 'anonymous' function, the difference from a normal defined function using the keyword `def`` is the syntax and usage.

The syntax is:

lambda[parameters]: [expresion]

Examples:

• A lambda function add 10 with any argument passed.

x = lambda a: a + 10
print(x(10))