Top 20 AWS CodeGuru Interview Questions and Answers

Amazon CodeGuru is a developer tool that offers insightful suggestions to enhance code quality and pinpoint the most expensive lines of code in an application. Integrate CodeGuru into your current software development workflow to automate code reviews throughout application development, continuously track the performance of applications in use, and provide visual cues and recommendations on how to enhance code quality, accelerate applications, and lower overall costs.

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Ques. 1): How do I sign up for the Amazon CodeGuru Reviewer programme?

Answer:

To incorporate Amazon CodeGuru Reviewer recommendations into your code pull requests, go to the Amazon CodeGuru portal. To begin receiving suggestions from Amazon CodeGuru Reviewers, go to the CodeGuru interface and follow the instructions to link your AWS CodeCommit, GitHub, GitHub enterprise, and BitBucket repositories. As soon as it is turned on, Amazon CodeGuru Reviewer will automatically make wise suggestions as comments on the pull requests you create for the connected repositories.

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Ques. 2): How is CodeGuru Reviewer's open-source code analysis software integrated?

Answer:

Amazon Rules from three sources are incorporated by CodeGuru Reviewer. (2) A managed version of Bandit, an open source code analysis tool designed to find security issues in Python code, is integrated into CodeGuru Reviewer (3) A managed version of Infer, an open source code analysis tool designed to find concurrency and other issues in Java code, is also integrated (4). CodeGuru Reviewer rules are integral to the service and designed to use machine learning and automated reasoning to analyse code for code quality and security issues. CodeGuru Reviewer has a straightforward pricing structure that incorporates all studies carried out and examines your code using rules from all sources (as appropriate to the programming language).

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Ques. 3): What kinds of problems does the Amazon CodeGuru Reviewer Security Detector pick up?

Answer:

You can get assistance with five different types of code security issues from CodeGuru Reviewer: (1) When accessing the APIs of various AWS services, such as AWS EC2 and KMS, you can follow the security best practises by consulting the AWS API Security Best Practices. (2) You may verify popular Java cryptography libraries like Javax using the Java and Python Crypto Library Best Practices. Python's Crypto.Cipher and any other built-in or external hashing or crypto modules (such as cryptography) should be initialised and invoked properly. (3) Cross-site scripting, LDAP injection, and route traversal injection are a few security concerns specific to online apps that Secure Web Applications can help you check for. (4) Internal security knowledge is brought through AWS Security Best Practices, such as AWS Crypto recommendations, to your use cases.

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Ques. 4): How can my CI/CD workflow be improved by using CodeGuru?

Answer:

You can quickly automate the code review process for both code quality and security recommendations by incorporating CodeGuru Reviewer into your CI/CD workflow. You can set it up to run during planned pipeline runs, pull/merge requests, or push. The integration makes sure you never miss a suggestion and are always keeping an eye on the quality of your code and any potential security flaws.

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Ques. 5): I've already included a lot of logging into my code. Do I need to profile anymore?

Answer:

Because logging can only monitor latency (not CPU utilisation) and is labor-intensive to implement because developers must log every function in an application (without affecting application performance), it is only useful for a small number of scenarios, leaving developers without the resources they need to effectively monitor and troubleshoot applications in production. Here's where profiling is useful: Amazon Regardless of scenarios, CodeGuru Profiler is made to gather information on everything that transpired during that application's activity. Code patterns in your live application that affect performance are automatically found by CodeGuru Profiler using a knowledge base of frequently encountered performance inefficiencies.

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Ques. 6): What is the operation of Amazon CodeGuru Profiler?

Answer:

An agent, the profiler service, and intelligent recommendations make up Amazon CodeGuru Profiler. The agent operates as an in-process thread as a component of your application and is launched along with it via the command line. Every five minutes, it sends data from each of your agent-running service instances to the profiler service, which aggregates it. The profile data is then made available by Amazon CodeGuru Profiler in the form of interactive flame graphs, allowing you to see how well your application is doing. Additionally, Amazon CodeGuru Profiler regularly analyses the profiled data, compares it to Amazon and performance engineering best practises, and proactively notifies you of performance issues with wise advice. It also uses machine learning to continuously analyze application runtime data and alerts when it detects anomalies in the application profile and points to the anomalous lines of code.

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Ques. 7): What can I do with information from a heap summary?

Answer:

Two situations make advantage of heap summarization. You can first look for potential memory leaks. A leak that could result in out-of-memory issues and programme crashes may be indicated by a steadily rising memory use curve for one or more object types. When you want to reduce the memory footprint of your programme, that is the second situation. You can choose where to focus in this situation by looking at the breakdown of memory usage by object type. For instance, you can concentrate your research and optimization efforts on the areas of your application that are in charge of allocating and referring objects of a given kind if you are aware that an unexpectedly high amount of memory has been associated with a particular object type.

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Ques. 8): What is Amazon CodeGuru capable of?

Answer:

Both Amazon CodeGuru Reviewer and Amazon CodeGuru Profiler are parts of Amazon CodeGuru. To enhance the quality of the code, Amazon CodeGuru Reviewer employs machine learning and automated reasoning to spot key problems and obscure defects throughout application development. With its ability to pinpoint the most expensive lines of code and optimise performance for applications operating in production, Amazon CodeGuru Profiler considerably lowers operational costs.

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Ques. 9): Amazon CodeGuru Reviewer: What is it?

Answer:

An automatic code review service called Amazon CodeGuru Reviewer finds serious errors and breaks from industry standards in Java and Python programming. It reads the lines of code contained in a pull request and offers insightful recommendations based on best practises discovered from both the Amazon codebase and significant open source projects. Amazon CodeGuru Reviewer offers actionable suggestions for enhancing code quality and effortlessly interacts with current code review workflows on popular source control platforms like GitHub, GitHub Enterprise, Bitbucket, and AWS CodeCommit.

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Ques. 10): The operation of the Amazon CodeGuru Reviewer Security Detector.

Answer:

The CodeGuru Reviewer Security Detector analyses code statically to provide a control-flow diagram of every potential execution scenario. Once data flow across the control-flow graph has been detected, possible problems requiring long sequences of operations involving several methods and classes may be found. For instance, even though initialization and use take place in different ways, the security detector can tell whether Javax.Crypto.KeyGenerator or Python's crypto.secrets, both of which are symmetric secret key generators, are initialised before use.

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Ques. 11): What is a profiling group?

Answer:

A logical grouping you make up is a profiling group. It serves as the limit of a single application. For instance, a profiling group in a microservices architecture would combine the profiles of the microservices you have given to it and create a single profile for all of them.

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Ques. 12): What effects does CodeGuru Profiler have on the efficiency of programmes running on AWS Lambda?

Answer:

The agent for Amazon CodeGuru Profiler makes use of the CPU and memory resources allotted to AWS Lambda operations. It is optimised to have a negligible effect on the performance of your programme while it is running as an in-process thread. Consider raising the resources if your application uses the majority of them in an AWS Lambda function so that the agent can operate as intended.

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Ques. 13): What details about memory profiling can Amazon CodeGuru Profiler offer?

Answer:

In CodeGuru Profiler, heap summary data is provided. Heap summaries provide a consolidated view of memory usage per object type (such as String, int, char[]), and custom types, over a specified period of time (usually 5 minutes). CodeGuru keeps track of both the total number of objects and their sizes. The timeline graph used to display these metrics makes it simple to see memory use trends and peaks by object type.

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Ques. 14): What kinds of problems does the Amazon CodeGuru Reviewer find?

Answer:

The Amazon CodeGuru Reviewer scans concurrent code for concurrency problems, potential race circumstances, malicious or unsanitized inputs, improper handling of sensitive data like credentials, resource leaks, and race conditions and deadlocks. Additionally, it recommends best practises for using AWS, Java, and Python and finds duplicate code that might be unified for improved code maintainability.

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Ques. 15): What distinguishes the Amazon CodeGuru Profiler from standard APMs and standalone profilers?

Answer:

Traditional APMs offer helpful information on application performance, monitoring, and tracing. These APM capabilities are complemented by Amazon CodeGuru Profiler, which offers visualisations of the application's runtime data in addition to suggestions for fixing any performance issues it finds. Additionally, it makes use of machine learning to find and notify you to anomalies in your application profile and identify the offending lines of code. Without having a strong expertise in performance engineering, you may use Amazon CodeGuru Profiler to quickly identify the areas of code that provide the most potential for speed optimization and cost reductions and obtain advice on how to address them. AWS Lambda, EC2 instances, containers, and on-premises serverless computing platforms are all profiled by Amazon CodeGuru Profiler. Additionally, while Amazon CodeGuru Profiler was created to run continuously in production, under production traffic levels, and without affecting the application, other standalone profilers are only intended to run in test environments. This is helpful when resolving operational problems in the field, especially those that arise while using bare metal hosts.

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Ques. 16): Can I use the same application to obtain CPU and memory information?

Answer:

Yes, CodeGuru Profiler will begin collecting CPU and memory information for your programme as soon as you enable memory profiling and begin profiling. For a certain application, all the CPU and memory data can be obtained from a single profiling group.

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Ques. 17): Is my code accessible to Amazon CodeGuru Reviewer?

Answer:

To produce recommendations, Amazon CodeGuru Reviewer requires read-only access to your code. Our top priorities are your trust, privacy, and the security of your content. To prevent unauthorised access to or disclosure of your content and to make sure that our use conforms with our promises to you, we put in place the necessary safeguards, including encryption in transit.

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Ques. 18): Which application categories may I profile?

Answer:

Apps hosted on Amazon EC2, containerized applications running on Amazon ECS and Amazon EKS, as well as serverless applications running on AWS Fargate and AWS Lambda, are all compatible with Amazon CodeGuru Profiler. Additionally, you can use Amazon CodeGuru Profiler locally.

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Ques. 19): How is the Amazon CodeGuru Reviewer taught to provide wise suggestions?

Answer:

Rule mining and supervised machine learning models that combine logistic regression and neural networks are used to train the Amazon CodeGuru Reviewer.

For instance, the Amazon CodeGuru Reviewer scrapes Amazon code bases using search techniques and location-sensitive models to look for pull requests that contain AWS API calls when training for divergence from AWS best practises. It examines code modifications meant to raise the level of the code and compares them to information in the documentation. As a result, when Reviewer examines your code, it generates a new set of guidelines that it suggests to you as best practises.

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Ques. 20): Why should I utilise the security detector for Amazon CodeGuru Reviewers?

Answer:

It's like having a security expert evaluate your code 24/7 using CodeGuru Reviewer Security Detector. Before deployment, it aids in identifying security best practises.

November 21, 2022

Top 20 AWS CodeArtifact Interview Questions and Answers

With the help of AWS CodeArtifact, businesses of any size can quickly and easily save, publish, and distribute the software packages they use in their software development process. This service is fully managed. The popular package managers and build tools Maven and Gradle (for Java), npm and yarn (for JavaScript), pip and twine (for Python), and NuGet are all compatible with CodeArtifact (.NET).

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Ques. 1): How can I begin using CodeArtifact?

Answer:

By setting up a fresh domain and repository through the AWS Management Console, SDKs, or CLI, you can start utilising CodeArtifact. Then, you can publish packages to your repository using well-known package managers and build tools like the npm or yarn CLI (for JavaScript), maven or gradle (for Java), pip (for Python), or NuGet (for.NET). Additionally, you may use your CodeArtifact repository, which keeps any packages used in this manner, to consume open-source packages from widely accessible repositories like npm registry, Maven Central, Python Package Index (PyPI), or NuGet.org.

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Ques. 2): What is a CodeArtifact repository?

Answer:

Each set of package versions in a CodeArtifact repository corresponds to a different group of assets. A single repository may contain packages of any supported kind since repositories are polyglot. Each repository provides APIs that may be used with programmes like the npm CLI, the Maven CLI (mvn), pip, and NuGet to fetch and publish packages. See Quotas in AWS CodeArtifact for information on resource limitations.

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Ques. 3): How can I use the AWS CLI to log into a CodeArtifact repository?

Answer:

Install the AWS CLI first, then setup the AWS login information for an IAM user or role that has the necessary access rights to CodeArtifact. To utilise the CodeArtifact GetAuthorizationToken API, use the CLI. This API sells authentication tokens, which may be requested by package managers and development tools and supplied in the HTTP Authorization header. The login command from the CLI makes use of GetAuthorizationToken and sets up a package manager to utilise this token for all requests. Developers who have used the docker CLI to communicate with Amazon ECR will be familiar with this approach because it is comparable to the get-login command offered by Amazon ECR.

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Ques. 4): Can I combine AWS CodeBuild with AWS CodeArtifact?

Answer:

Yes. In your CodeBuild project setup, you may define which CodeArtifact repositories to utilise for downloading and publishing packages. Client tools for all the package types supported by CodeArtifact are included in the available CodeBuild images. With the help of the build's IAM role, CodeBuild configures the build tool or package manager to utilise the given repository and obtain a CodeArtifact auth token at build time. Additionally, you may select which build artefacts should be added to your CodeArtifact repository when the build is finished. When the contents of a CodeArtifact repository change, CloudWatch Events are released, which may be used to start a CodeBuild build.

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Ques. 5): Permissions may be enabled at the package level, right?

Answer:

Yes. CodeArtifact supports write permissions at the package level. These may be set up by adding clauses that identify a package ARN as the resource in a repository resource policy document. CodeArtifact only supports read permissions at the repository level, meaning an IAM principal may either read every package in a repository or none of them.

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Ques. 6): What is a domain in CodeArtifact?

Answer:

A domain is a feature unique to CodeArtifact that enables the management of several CodeArtifact repositories owned by the same company across various AWS accounts. For instance, a company may establish a central repository for team package sharing and project-level repositories to house package usage-specific storage.

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Ques. 7): What is the price of CodeArtifact?

Answer:

There are no up-front costs or commitments with CodeArtifact. You only pay for the amount of data transported out of an AWS Region, the volume of requests performed, and the software packages stored. A monthly free tier for storage and requests is offered by CodeArtifact.

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Ques. 8): How can I get a CodeArtifact artefact back?

Answer:

Use tools that are inherent to the language to fetch artefacts. Run the CodeArtifact CLI "login" command, followed by npm install webpack, to install, for instance, the npm package webpack and all of its dependencies. npm retrieves webpack from CodeArtifact and resolves dependencies using data from webpack's package. json file, after which it pulls all necessary dependencies recursively from CodeArtifact.

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Ques. 9): My packets are encrypted using CodeArtifact.

Answer:

Yes. All of the packages that CodeArtifact stores are secured at rest and in transit using TLS and AES-256 symmetric key encryption, respectively. Both the AWS Key Management Service (KMS) client controlled CMKs and the AWS managed CMKs are supported by CodeArtifact.

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Ques. 10): In CodeArtifact, how do I build repositories?

Answer:

Using the console wizard, the AWS SDKs or CLI, or programmatically, you may build repositories.

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Ques. 11): How can I set up a CodeArtifact repository to fetch packages from outside package archives like the npm registry?

Answer:

Create a repository with an external connection on the CodeArtifact console to fetch packages from a public repository like the npm registry. The AssociateExternalConnection API may also be used to link a public repository with a CodeArtifact repository. If the desired packages are not already available, CodeArtifact fetches and caches them from outside repositories whenever a package request is made.

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Ques. 12): How do I publish artifacts to CodeArtifact?

Answer:

Using language-specific tools like npm or yarn (for JavaScript), maven or gradle (for Java), twine (for Python), or NuGet, you may publish artefacts (.NET). For instance, using npm, releasing a new package version necessitates running two commands: To upload the package to the repository, first use the "login" command in the CodeArtifact CLI, and then run npm publish. As part of a continuous integration (CI) workflow, AWS CodeBuild may publish new package versions by executing the same instructions.

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Ques. 13): AWS CodeArtifact and AWS CodePipeline are compatible, right?

Answer:

Yes. When a package kept in a CodeArtifact repository changes, as when a new version of the package is released, you can use Amazon EventBridge to start a CodePipeline build.

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Ques. 14): Can I make my repositories accessible across accounts?

Answer:

Yes. Resource policies are supported by CodeArtifact repositories to allow cross-account access. By using the console or AWS CLI, you may add a resource policy.

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Top 20 AWS EC2 Auto Scaling Interview Questions and Answers

To assist you make sure you have the appropriate number of Amazon EC2 instances available to handle the load for your application, Amazon EC2 Auto Scaling is a fully managed solution designed to deploy or terminate Amazon EC2 instances automatically. Through fleet management for EC2 instances, which recognises and replaces unhealthy instances, and by automatically scaling your Amazon EC2 capacity up or down in accordance with conditions you establish, Amazon EC2 Auto Scaling aids you in maintaining application availability. To preserve performance and lower costs, you may utilise Amazon EC2 Auto Scaling to automatically increase the number of Amazon EC2 instances during surges in demand.

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Ques. 1): What is target tracking?

Answer:

You may quickly configure dynamic scaling for your application using target tracking, a new kind of scaling strategy. Goal tracking allows you to choose a load measure for your application, such as request count or CPU usage, define a target value, and Amazon EC2 Auto Scaling will change the number of EC2 instances in your ASG as necessary to meet that target. It works like a thermostat for your house, automatically altering the system to maintain the appropriate temperature. For instance, you may set goal tracking so that 50% of your fleet of web servers' CPUs are always in use. Then, to maintain an average CPU usage of 50%, Amazon EC2 Auto Scaling starts or terminates EC2 instances as necessary.

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Ques. 2): How does capacity balance work in Amazon EC2 Auto Scaling?

Answer:

A best practise for well-architected applications is to balance resources across Availability Zones, since this considerably improves aggregate system availability. When you specify several zones in your EC2 Auto Scaling group settings, Amazon EC2 Auto Scaling automatically distributes EC2 instances across the configured zones. New instances are always started by Amazon EC2 Auto Scaling so that the fleet as a whole is balanced between zones as evenly as feasible. Additionally, Amazon EC2 Auto Scaling only starts up in Availability Zones with capacity for the requested instance type.

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Ques. 3): What is meant by the phrase "stateful instances"?

Answer:

A stateful instance is one that contains data on it that is unique to that instance when the term is used. A stateful instance's data (or state information) is often lost when the instance is terminated. You might want to think about enabling instance protection to stop Amazon EC2 Auto Scaling from terminating a stateful instance or utilising lifecycle hooks to transfer the data off of it before it is terminated.

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Ques. 4): When should I choose AWS Auto Scaling over Amazon EC2 Auto Scaling?

Answer:

To handle scaling for numerous resources across different services, you should utilise AWS Auto Scaling. Using specified scaling methods, AWS Auto Scaling enables you to define dynamic scaling policies for several EC2 Auto Scaling groups or other resources. It takes less time to manage scaling policies for all of the scalable resources in your application using AWS Auto Scaling than it does using individual service consoles for each resource. Additionally, it is simpler since AWS Auto Scaling comes with predefined scaling methods that make it simpler to set up scaling policies.

If you simply need to scale Amazon EC2 Auto Scaling groups or if you only care about keeping the health of your EC2 fleet, you should utilise EC2 Auto Scaling. If you need to set up scheduled or step scaling policies, or if you need to build or configure Amazon EC2 Auto Scaling groups, you should also utilise EC2 Auto Scaling (as AWS Auto Scaling supports only target tracking scaling policies).

The EC2 interface, the Auto Scaling API, or CloudFormation may all be used to establish and configure EC2 Auto Scaling groups outside of AWS Auto Scaling. You may define dynamic scaling policies for your current EC2 Auto Scaling groups with the aid of AWS Auto Scaling.

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Ques. 5): A launch configuration is what?

Answer:

An EC2 Auto Scaling group will utilise a launch configuration as a template for launching EC2 instances. A key pair, one or more security groups, a block device mapping, the ID of the Amazon Machine Image (AMI), the instance type, and other details are all specified when creating a launch configuration for an instance. You provided the identical details to launch the instance if you've launched an EC2 instance before. An EC2 Auto Scaling group's launch settings must be specified when the group is created. You can use several EC2 Auto Scaling groups to describe your launch settings. One launch configuration may only be specified for one EC2 Auto Scaling group at a time, and once a launch configuration has been defined, it cannot be changed. Therefore, you must first construct a launch configuration before updating your EC2 Auto Scaling group with the new launch setting if you wish to alter the launch configuration for your EC2 Auto Scaling group. New instances are launched using the new configuration settings when the launch configuration for your EC2 Auto Scaling group is changed, but existing instances are unaffected.

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Ques. 6): Is there a method to create a volume just using Amazon EC2 Auto Scaling without installing an instance?

Answer:

Each time a new instance is introduced, a volume is associated with it. When a volume is nearly full, Amazon EC2 Auto Scaling doesn't instantly add another. A volume can be added to an existing instance using the EC2 API.

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Ques. 7): What distinguishes Predictive Scaling Policy from AWS Auto Scaling Plan's Predictive Scaling?

Answer:

In EC2 Auto Scaling, Predictive Scaling Policy introduces the same prediction mechanism that is available through AWS Auto Scaling plan as a native scaling policy. As with other scaling policies like Simple Scaling or Target Tracking, you may utilise predictive scaling directly using the AWS Command Line Interface (CLI), EC2 Auto Scaling Management Console, and AWS SDKs. Predictive scaling does not need the creation of an AWS Auto Scaling plan.

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Ques. 8): An EC2 Auto Scaling group (ASG) is what, exactly?

Answer:

For the purposes of fleet management and dynamic scaling, an Amazon EC2 Auto Scaling group (ASG) is a collection of EC2 instances that have similar features and are considered as a logical grouping. If a single application runs on many instances, for instance, you could wish to raise the number of instances in that group to improve the performance of the application or decrease the number of instances to save expenses during periods of low demand. Even if an instance falls unwell or depending on criteria you provide, Amazon EC2 Auto Scaling will automatically alter the number of instances in the group to keep a fixed number of instances.

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Ques. 9): How do lifecycle hooks work?

Answer:

With the use of lifecycle hooks, you may intervene before an instance starts running or before it is destroyed. If you are not building your software environment into an Amazon Machine Image, this can be extremely helpful (AMI). For instance, launch hooks can configure an instance's software to make sure it is ready to handle traffic before Amazon EC2 Auto Scaling connects it to your load balancer. Connecting the launch hook to an AWS Lambda function that calls RunCommand on the instance is one method to do this. Terminate hooks may be helpful for gathering crucial information from an instance before it disappears. For example, you could use a terminate hook to preserve your fleet’s log files by copying them to an Amazon S3 bucket when instances go out of service.

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Ques. 10): How can I manage who has access to the resources in Amazon EC2 Auto Scaling?

Answer:

You may perform the following things thanks to the integration between Amazon EC2 Auto Scaling and AWS Identity and Access Management (IAM), a service:

Create users and groups under the AWS account for your company.
Provide each user with their own security credentials under your AWS account.
Manage the rights each user has to access AWS resources for tasks.
Share your AWS resources with users of other AWS accounts.
Define the users or services that can take responsibilities in your AWS account.
Use your company's current IDs to provide rights to carry out actions utilising AWS resources.
Create an IAM policy, for instance, allowing the Managers group to utilise only the DescribeAutoScalingGroups.

For example, you could create an IAM policy that grants the Managers group permission to use only the DescribeAutoScalingGroups, DescribeLaunchConfigurations, DescribeScalingActivities, and DescribePolicies API operations. Users in the Managers group could then use those operations with any Amazon EC2 Auto Scaling groups and launch configurations. With Amazon EC2 Auto Scaling resource-level permissions, you can restrict access to a particular EC2 Auto Scaling group or launch configuration.

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Ques. 11): What if an Availability Zone does not offer the instance types I prefer?

Answer:

The launches will be retargeted to other Availability Zones included in the Auto Scaling group if none of the specified instance types are available in one. If all instance types are not accessible in an Availability Zone, Auto Scaling will retarget and always prefer to maintain your computing distributed across Availability Zones.

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Ques. 12): What distinguishes fleet management from dynamic scaling?

Answer:

You have a "fleet" if your application uses Amazon EC2 instances, as the term implies. The capability that automatically replaces unhealthy instances and keeps your fleet operating at the desired capacity is referred to as fleet management. The fleet management of Amazon EC2 Auto Scaling makes sure that both the instances themselves are operating properly and that your application can handle traffic. When Auto Scaling notices a failed health check, it can automatically switch out the instance.

The capability of Amazon EC2 Auto Scaling that automatically raises or reduces capacity based on demand or other variables is referred to as its dynamic scaling capabilities. For example, if your CPU spikes above 80% (and you have an alarm setup) Amazon EC2 Auto Scaling can add a new instance dynamically.

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Ques. 13): What constitutes an instance that is "unhealthy"?

Answer:

An unhealthy instance is one that fails a user-configured ELB health check or where the hardware has degraded for whatever reason (bad disc, etc.). Each individual EC2 instance is periodically given a health check by Amazon EC2 Auto Scaling. If the instance is linked to an Elastic Load Balancing load balancer, it may additionally execute ELB health checks.

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Ques. 14): How may a damaged instance be replaced by Amazon EC2 Auto Scaling?

Answer:

Amazon EC2 Auto Scaling immediately kills an unhealthy instance and substitutes a new one when it fails a health check. Amazon EC2 Auto Scaling gently detaches the compromised instance from the load balancer if you're using one before generating a new one and connects it to the load balancer. When an instance has to be replaced, nothing needs to be done manually because everything is handled automatically.

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Ques. 15): Is it possible to scale instances across several purchasing choices with a single ASG?

Answer:

Yes. In a single Auto Scaling Group, you can provide and automatically scale EC2 capacity across several EC2 instance types, Availability Zones, and On-Demand, RIs, and Spot purchasing options. You may choose which instance types are best for your application, set the appropriate split between On-Demand and Spot capacity, and indicate your preferences for how EC2 Auto Scaling should allocate the ASG capacity within each purchase model.

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Ques. 16): Can I scale out my ASG on Spot instances after establishing a basic capacity using On-Demand instances and RIs?

Answer:

Yes. You may set the group's basic capacity to be met by On-Demand instances when creating an ASG to mix buying models. EC2 Auto Scaling makes ensuring that the basic capacity is filled with On-Demand instances when the ASG scales in or out, and that anything beyond that is filled with either entirely Spot instances or a specific % mix of On-Demand or Spot instances.

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Ques. 17): How long does it take Amazon EC2 Auto Scaling to launch a new instance in the inService state after identifying a malfunctioning server?

Answer:

Within minutes, everything is ready. The bulk of replacements take place in within five minutes, and on average it takes considerably less time. It depends on a number of variables, including how long it takes for your instance's AMI to boot up.

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Ques. 18): Can I alter an ASG's setup to reflect new characteristics for merging buying models and defining multiple instance types?

Answer:

Yes. Customers can update an existing ASG to change one or all parameters related to combining purchasing models and specifying multiple instance types, including instance types, priority order for On-Demand instances, percentage split between On-Demand and Spot instances, and allocation strategy. This is similar to how other ASG parameters can be changed.

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Ques. 19): What advantages does utilising Amazon EC2 Auto Scaling offer?

Answer:

Your Amazon EC2 instance availability is maintained with the aid of Amazon EC2 Auto Scaling. Using Amazon EC2 Auto Scaling, you can automatically replace any Amazon EC2 instances that become faulty, whether you are operating one or hundreds of them. This makes sure that your application has the expected amount of computing power. To reduce the need to manually provision Amazon EC2 capacity in advance, you may utilise Amazon EC2 Auto Scaling to automatically expand your Amazon EC2 fleet by following the demand curve for your applications. For instance, when the average usage of your Amazon EC2 fleet is high, you may establish a condition to add new instances in increments to the ASG. Likewise, when CPU utilisation is low, you can set a condition to remove instances in increments. Additionally, you may utilise Elastic Load Balancing (ELB) to distribute traffic to your instances inside the ASG and Amazon CloudWatch to transmit alarms that will start scaling actions. Predictive Scaling strategy may be used to proactively expand capacity in advance of impending demand if you have predicted load variations. You can run your Amazon EC2 fleet at maximum utilisation thanks to Amazon EC2 Auto Scaling.

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Ques. 20): How would users be routed to the other servers in a group in the event of a breakdown without elastic load balancing (ELB)?

Answer:

You can connect to Route53 (which Amazon EC2 Auto Scaling does not currently support out of the box, but many customers use). You may also use your own reverse proxy or service discovery tools for internal microservices.

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