Top 20 AWS CodeDeploy Interview Questions and Answers

Software deployments to multiple compute services, including Amazon Elastic Compute Cloud (EC2), Amazon Elastic Container Service (ECS), AWS Lambda, and your on-premises servers, are automated via the completely managed deployment solution AWS CodeDeploy. By automating software deployments with CodeDeploy, human, prone to error processes are no longer necessary.

Ques. 1): With AWS CodeDeploy, what kinds of apps may be deployed?

Answer:

Any kind of programme can be deployed with AWS CodeDeploy. The files to copy and the scripts to run on each instance during the deployment are specified when using AWS CodeDeploy. Because AWS CodeDeploy supports any custom deployment logic, regardless of the programming language or architecture used, scripts can be used.

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Ques. 2): What platforms is AWS CodeDeploy compatible with?

Answer:

Numerous different operating systems are supported by AWS CodeDeploy. Agents are available from AWS CodeDeploy that have been tried out on Red Hat Enterprise Linux, Amazon Linux, Ubuntu Server, and Microsoft Windows Server. The AWS CodeDeploy agent is free source software, so you can use it with other operating systems.

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Ques. 3): What exactly is AWS CodeDeploy?

Answer:

AWS CodeDeploy is a service that automates the deployment of code to any instance, including instances hosted on-premises and in Amazon EC2. You can deliver new features more quickly, avoid downtime during deployment, and handle the complexity of updating your apps with AWS CodeDeploy. By automating deployments, AWS CodeDeploy eliminates the need for labor-intensive manual processes. The service scales with your infrastructure, allowing you to quickly deploy to one instance or thousands.

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Ques. 4): What distinguishes Amazon CodeDeploy from other AWS management and deployment tools like AWS Elastic Beanstalk and AWS OpsWorks?

Answer:

AWS CodeDeploy is a building block tool designed to assist developers in deploying and updating software on any instance, including instances running on-premises and through Amazon EC2. End-to-end application management solutions are provided by AWS Elastic Beanstalk and AWS OpsWorks.

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Ques. 5): What is a deployment group?

Answer:

The AWS CodeDeploy entity for grouping EC2 instances or AWS Lambda functions in a CodeDeploy deployment is called a deployment group. You target a set of instances connected to an application while using EC2 for deployments. By naming an Auto Scaling group, a tag, or both, you can add instances to a deployment group. In an Amazon Lambda deployment, a deployment group specifies a set of AWS CodeDeploy configurations, such as alarms and rollbacks, for upcoming serverless Lambda deployment to the group.

An application can have numerous deployment groups, such as staging and production. See Dealing with Amazon EC2 Tags in the Console for more details on tags.

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Ques. 6): What are the typical processes involved in utilising AWS CodeDeploy to launch an application?

Answer:

The common steps taken during a deployment are depicted in the diagram below. The first setup step for each application is often creating an application and a deployment group (for definitions of these words, see the Concepts section). Uploading a revision and deploying it are repeatable tasks.

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Ques. 7): How can I use AWS CodeDeploy to deploy an application from my source control system?

Answer:

If you use GitHub, you can publish a revision from your repository straight to instances in.zip,.tar, or.tar.gz format. When deploying code from other source control systems, you can provide the Amazon S3 location and bundle the revision and upload it to an Amazon S3 bucket in a.tar.gz,.zip, or.tar.xz format. Make sure the post-build artefacts are present in the GitHub repository or the Amazon S3 bucket if your application requires a build phase.

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Ques. 8): How does Auto Scaling integrate with AWS CodeDeploy?

Answer:

To guarantee that freshly started instances always receive the most recent version of your application, you can join an Auto Scaling group with a deployment group. Each time an Amazon EC2 instance for that Auto Scaling group is launched, it is first placed in a Pending state before the deployment of the most recent successful revision for that deployment group is triggered on that Amazon EC2 instance. The Amazon EC2 instance's state is updated to InService if the deployment is successful. A new Amazon EC2 instance is launched in the Pending state and a deployment is initiated for the newly launched EC2 instance if the deployment fails.

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Ques. 9): Can I manage access to AWS CodeDeploy using AWS Identity and Access Management (IAM)?

Answer:

Yes. Resource-level permissions are supported by AWS CodeDeploy. You can select which user has access and to what actions for each AWS CodeDeploy resource. For instance, you can configure an IAM policy to only allow users to list revisions for other apps while allowing users to install a specific application. Hence, you may stop users from unintentionally changing the incorrect programme.

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Ques. 10): How are AWS CodeDeploy applications distributed across many regions?

Answer:

When performing deployments, AWS CodeDeploy uses AWS resources in the same area. Create the application in each of your target locations, copy the application bundle to an Amazon S3 bucket there, and then begin the deployments using either a serial or parallel rollout across the regions to spread the deployments out.

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Ques. 11): Can my current tool chain work with AWS CodeDeploy?

Answer:

Yes. Many configuration management, continuous integration, and deployment, as well as source control solutions are compatible with AWS CodeDeploy.

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Ques. 12): How do revisions work?

Answer:

A revision is a particular version of deployable material, which includes an AppSpec file, source code, post-build artefacts, web pages, executable files, and deployment scripts. A revision can be accessed via the AWS CodeDeploy Agent from GitHub or an Amazon S3 bucket.

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Ques. 13): What is a deployment configuration?

Answer:

A deployment configuration outlines the actions that should be taken during deployment, including how to deal with failure, through the use of a deployment group. To deploy with zero downtime to multi-instance deployment groups, you can utilise a deployment configuration. You can define this in your deployment configuration, for instance, if your application requires at least 50% of the instances in a deployment group to be active and handling traffic, preventing downtime from a deployment. Amazon CodeDeploy will deploy to one instance at a time by default if no deployment setting is linked to either the deployment or the deployment group.

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Ques. 14): How will my configuration management tool and AWS CodeDeploy interact?

Answer:

Your configuration management tool can be launched from any AppSpec file deployment lifecycle event hook. For instance, you can define a Chef recipe that you wish to execute as part of a deployment in the relevant AppSpec file deployment lifecycle event hook. The AWS CodeDeploy agent can also be installed on instances using your configuration management tool. for examples that demonstrate integrating configuration management tools like Chef, Puppet, Ansible, and Saltstack with AWS CodeDeploy.

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Ques. 15): How can I keep track of a deployment's progress?

Answer:

The Amazon Management Console, AWS Command Line Interface (AWS CLI), AWS SDKs, and AWS CodeDeploy APIs can all be used to monitor the progress of a deployment.

You may view the general deployment status and then drill down to view the status of every instance and every deployment lifecycle event for the instance. Also, you can examine the log entries associated with every failure, which makes it simple to troubleshoot deployment difficulties without logging into the instance.

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Ques. 16): How can I get notified or alerted when something happens in AWS CodeDeploy?

Answer:

Events that affect your deployments can have notifications created for them. Amazon SNS notifications will be used for notifications. Each notification will have a link to the resources whose event caused it, along with a status message. There is no additional price for notifications, but you can be charged for other AWS services that notifications uses, including Amazon SNS. Consult the alerts user guide to understand how to get started. Customers can also set up notifications to go to their Slack channels or Amazon Chime chat rooms when utilising the AWS Chatbot.

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Ques. 17): An application is what?

Answer:

A group of instances can be served by deploying an application, which is a bundle of software and configuration. The instances in the group often use the same programme. For instance, if you have a sizable distributed system, it's likely that the web layer will be one application and the data tier will be another.

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Ques. 18): What is an AppSpec file?

Answer:

An AppSpec file is a configuration file that details the scripts to run and the files to copy. You place the YAML-formatted AppSpec file in the root directory of your revision. The AppSpec file has two sections and is used by the Amazon CodeDeploy Agent. The source files in your revision that will be copied as well as the final destination folder are specified in the files section for each instance. The location of the scripts to run during each stage of the deployment is specified in the hooks section as relative paths beginning at the revision bundle's root. A deployment lifecycle event is the name given to each stage of a deployment.

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Ques. 19): What modifications must I make to my code before using AWS CodeDeploy?

Answer:

Your code doesn't require any modifications. You only include a configuration file (known as an AppSpec file) in the revision bundle's root directory that lists the files to copy and the scripts to run.

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Ques. 20): How can I restore a previously revised application?

Answer:

You only need to deploy the prior revision in order to roll back an application to it. There is no distinction between redeploy and roll back because AWS CodeDeploy keeps track of the files that were copied for the current version and deletes them before beginning a new deployment. You must, however, confirm that it is possible to roll back the prior updates.

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Top 20 AWS Timestream Interview Questions and Answers

Amazon Timestream is serverless and scales up or down automatically to adjust capacity and performance, so you can concentrate on creating your applications without having to worry about managing the underlying infrastructure.

For IoT and operational applications, Amazon Timestream is a quick, scalable, and serverless time series database service that makes it simple to store and analyse trillions of events per day up to 1,000 times more quickly and for as little as 1/10th the price of relational databases. By storing recent data in memory and transferring historical data to a storage tier that is more cost-effective depending on user-defined parameters, Amazon Timestream helps you manage the lifecycle of time series data more efficiently. You can access and analyse recent and historical data together without identifying its location thanks to Amazon Timestream's adaptive query engine. With built-in time series analytics capabilities, Amazon Timestream enables you to quickly spot patterns and trends in your data. Amazon Timestream is serverless and automatically scales up or down to adjust capacity and performance, so you don’t need to manage the underlying infrastructure, freeing you to focus on building your applications.

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Ques. 1): What performance can I expect from Amazon Timestream?

Answer:

Amazon Timestream offers near real-time latencies for data ingestion. Amazon Timestream’s built-in memory store is optimized for rapid point-in-time queries, and the magnetic store is optimized to support fast analytical queries. With Amazon Timestream, you can run queries that analyze tens of gigabytes of time-series data from the memory store within milliseconds, and analytical queries that analyze terabytes of time-series data from the magnetic store within seconds. Scheduled queries further improve query performance by calculating and storing the aggregates, rollups, and other real-time analytics used to power frequently accessed operational dashboards, business reports, applications, and device-monitoring systems.

As your applications continue to send more data, Amazon Timestream automatically scales to accommodate their data ingestion and query needs. You can store exabytes of data in a single table. As your data grows over time, Amazon Timestream uses its distributed architecture and massive amounts of parallelism to process larger and larger volumes of data while keeping query latencies almost unchanged.

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Ques. 2): Before transmitting data to Amazon Timestream, do I need to build a schema?

Answer:

No. A set of dimensional properties and measures are used by Amazon Timestream to dynamically generate a table's schema. This provides a flexible and gradual description of the schema that may be changed whenever necessary without affecting availability.

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Ques. 3): How is data stored by Amazon Timestream?

Answer:

A timestamp is used by Amazon Timestream to organise and store time-series data, and dimensional attributes are used to arrange data over time. For further information, visit Werner Vogel's blog. By simply defining data retention policies to automatically migrate data from the memory store to the magnetic storage as it approaches the configured age, you may use Amazon Timestream to automate data lifecycle management.

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Ques. 4): Can I use Amazon Timestream to automatically roll up, combine, or preprocess my data?

Answer:

For calculating and storing aggregates, rollups, and other real-time analytics required to power frequently viewed operational dashboards, business reports, applications, and device monitoring systems, Amazon Timestream's scheduled queries provide a fully managed, serverless, and scalable solution.

To calculate aggregates, rollups, and other real-time analytics on your incoming data, you just describe the real-time analytics queries with scheduled queries. Amazon Timestream then periodically and automatically runs these queries and reliably stores the query results into a different table. Your dashboards, reports, programmes, and monitoring tools can then be configured to just query the destination tables rather than the much larger source tables that hold the incoming time-series data. This leads to performance and cost reductions by an order of magnitude because the destination tables contain much less data than the source tables, thereby offering faster and cheaper data access and storage.

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Ques. 5): Can I utilise an Amazon Virtual Private Cloud (VPC) with Amazon Timestream?

Answer:

Using VPC endpoints, you can connect to Amazon Timestream from your Amazon VPC. Without a network address translation (NAT) instance or an internet gateway, Amazon VPC endpoints offer dependable connectivity to Amazon Timestream APIs.

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Ques. 6): What are data from time series?

Answer:

A time series is a collection of data points that are collected over a period of time to track changing events. Examples include changes in stock prices over time, temperature readings over time, and an EC2 instance's CPU usage over time. Each data point in a time-series consists of an event that changes over time, one or more attributes, and a timestamp. This data is utilised to spot abnormalities, find chances for improvement, and gain insights into the functionality and health of an application. DevOps engineers, for instance, might be interested in data that tracks changes in infrastructure performance metrics, manufacturers might be interested in IoT sensor data that tracks equipment changes throughout a facility, and online marketers might be interested in clickstream data that records a user's website navigation over time. Time series data must be cost-effectively collected in close to real-time, produced in extremely high volumes from a variety of sources, and stored in a way that makes it easy to organise and analyse.

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Ques. 7): How can I transmit information to Amazon Timestream?

Answer:

From linked devices, IT systems, and industrial machinery, you may gather time series data and write it into Amazon Timestream. Utilizing data gathering tools like AWS IoT Core, Amazon Kinesis Data Analytics for Apache Flink, or Telegraf, as well as the AWS SDKs, you can submit data to Amazon Timestream.

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Ques. 8): What advantages does the memory store offer?

Answer:

The memory store used by Amazon Timestream is a write-optimized store that receives and deduplicates time series data. Additionally, it accepts and handles data that arrives late from systems and programmes with erratic connectivity. For point-in-time queries that need low latency, the memory storage is additionally optimised.

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Ques. 9): Does Amazon Timestream support data encryption?

Answer:

In Amazon Timestream, data is always encrypted, whether at rest or in transit. Amazon Timestream also enables you to specify an AWS KMS customer managed key (CMK) for encrypting data in the magnetic store.

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Ques. 10): What is the operation of Amazon Timestream?

Answer:

The whole architecture of Amazon Timestream is focused on gathering, storing, and processing time series data. Because of the fully decoupled data intake, storage, and query processing systems supported by its serverless design, Amazon Timestream can provide almost infinite scale for your application's requirements. A Timestream table's schema is dynamically built based on the properties of the incoming time series data, allowing for flexible and gradual schema definition, as opposed to being pre-defined at table creation time. When data is saved, Amazon Timestream divides it into sections based on time and other factors, speeding up data access with a specially designed index. Additionally, by providing an in-memory storage for recent data, a magnetic store for historical data, and by allowing configurable rules to automatically migrate data from the memory store to the magnetic store as it reaches a specific age, Amazon Timestream automates the lifecycle management of data. As a result, you can quickly and effectively use SQL to extract insights from your data using Amazon Timestream's purpose-built adaptive query engine, which can seamlessly access and integrate data across storage tiers without requiring you to identify the data location. Finally, Amazon Timestream makes it simple for you to integrate Amazon Timestream into your time series solutions by integrating easily with your choice data collecting, visualisation, analytics, and machine learning services.

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Ques. 11): How do I handle data about upcoming or delayed arrivals?

Answer:

Data with a timestamp in the past is referred to as late arrival data. Data with a timestamp in the future is referred to as future data. Both types are accessible and storable via Amazon Timestream.

Simply enter the information to be stored as late arrival data into Amazon Timestream, and the service will choose whether to write it to the memory store or the magnetic store depending on the timestamp and the specified data retention for the memory and magnetic stores.

Model your data as a multi-measure record with the future timestamp represented as a measure inside the record if you want to store future data.

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Ques. 12): Does Amazon Timestream meet any compliance certification ready requirements?

Answer:

Amazon Timestream complies with the HIPAA Common Security Framework (CSF), the ISO (9001, 27001, 27017, and 27018), PCI DSS, FedRAMP (Moderate), and FedRAMP (High). Additionally, Amazon Timestream is covered by SOC 1 and SOC 2 and SOC 3 reports from AWS.

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Ques. 13): What is the price of Amazon Timestream?

Answer:

You only pay for the usage you make using Amazon Timestream. Writes, data storage, and data scanned by queries are all invoiced separately. Your writing, storage, and query capacities are automatically scaled by Amazon Timestream based on usage. Each table has a data retention policy that you may customise, and you can decide whether to save data magnetically or in memory.

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Ques. 14): What advantages does the magnetic store offer?

Answer:

The magnetic store offered by Amazon Timestream is a read-friendly repository for the past. Hundreds of terabytes of data can be quickly scanned by analytical queries that use the magnetic store's enhanced performance.

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Ques. 15): What machine learning (ML), analytics, and visualisation technologies are compatible with Amazon Timestream?

Answer:

Grafana and Amazon QuickSight are two tools you can use with Amazon Timestream to display and analyse time-series data. For your ML requirements, you can alternatively combine Amazon Timestream and Amazon SageMaker.

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Ques. 16): How do I use Grafana and Amazon Timestream?

Answer:

Grafana is a multi-platform, open-source analytics and interactive visualisation tool that you can use to view your Amazon Timestream time-series data and generate alerts.

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Ques. 17): What is the scaling of Amazon Timestream?

Answer:

Because of the fully decoupled data intake, storage, and query processing systems supported by Amazon Timestream's serverless architecture, which can scale separately, Amazon Timestream can provide essentially infinite scaling for your application's requirements.

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Ques. 18): How do I use Amazon Timestream to query data?

Answer:

Your time series data kept in Amazon Timestream can be queried using SQL. SQL can be used to perform interpolation, regression, and smoothing time series analytics functions.

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Ques. 19): How can I use Amazon Kinesis Data Analytics to transfer data to Amazon Timestream?

Answer:

Your time series data from Amazon Kinesis Data Analytics may be directly transferred into Amazon Timestream using Apache Flink.

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Ques. 20): What time stamp is applied to data when it is ingested by Amazon Timestream?

Answer:

The timestamp of the time series event being written into the database is what Amazon Timestream utilises. It provides nanosecond-level granularity timestamps.

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