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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Configuration Management and Infrastructure as Code | 17% | - Infrastructure provisioning and automation
|
| Topic 2: Resilient Cloud Solutions | 15% | - High availability and fault tolerance design
|
| Topic 3: SDLC Automation | 22% | - CI/CD pipeline design and implementation
|
| Topic 4: Monitoring and Logging | 15% | - Observability and metrics
|
| Topic 5: Security and Compliance Automation | 13% | - Security automation in CI/CD and infrastructure
|
| Topic 6: Incident and Event Management | 18% | - Operational response and recovery
|
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NEW QUESTION # 396
A company has a mobile application that makes HTTP API calls to an Application Load Balancer (ALB). The ALB routes requests to an AWS Lambda function. Many different versions of the application are in use at any given time, including versions that are in testing by a subset of users. The version of the application is defined in the user-agent header that is sent with all requests to the API.
After a series of recent changes to the API, the company has observed issues with the application. The company needs to gather a metric for each API operation by response code for each version of the application that is in use. A DevOps engineer has modified the Lambda function to extract the API operation name, version information from the user-agent header and response code.
Which additional set of actions should the DevOps engineer take to gather the required metrics?
Answer: D
Explanation:
"Note that the metric filter is different from a log insights query, where the experience is interactive and provides immediate search results for the user to investigate. No automatic action can be invoked from an insights query. Metric filters, on the other hand, will generate metric data in the form of a time series. This lets you create alarms that integrate into your ITSM processes, execute AWS Lambda functions, or even create anomaly detection models." https://aws.amazon.com/blogs/mt/quantify-custom-application-metrics-with-amazon-cloudwatch-logs-and-metric-filters/
NEW QUESTION # 397
A DevOps engineer needs to implement integration tests into an existing AWS CodePipelme CI/CD workflow for an Amazon Elastic Container Service (Amazon ECS) service. The CI/CD workflow retrieves new application code from an AWS CodeCommit repository and builds a container image. The CI/CD workflow then uploads the container image to Amazon Elastic Container Registry (Amazon ECR) with a new image tag version.
The integration tests must ensure that new versions of the service endpoint are reachable and that vanous API methods return successful response data The DevOps engineer has already created an ECS cluster to test the service Which combination of steps will meet these requirements with the LEAST management overhead? (Select THREE.)
Answer: C,D,E
Explanation:
Add a Deploy Stage to the Pipeline, Configure Amazon ECS as the Action Provider:
By adding a deploy stage to the pipeline and configuring Amazon ECS as the action provider, the pipeline can automatically deploy the new container image to the ECS cluster.
This ensures that the service is updated with the new image tag, making the new version of the service endpoint reachable.
Reference: AWS CodePipeline with ECS
Update the Image Build Pipeline Stage to Output an imagedefinitions.json File that References the New Image Tag:
The imagedefinitions.json file provides the necessary information about the container images and their tags for the ECS task definitions.
Updating the pipeline to output this file ensures that the correct image version is deployed.
Example imagedefinitions.json
[
{
"name": "container-name",
"imageUri": "123456789012.dkr.ecr.region.amazonaws.com/my-repo:my-tag"
}
]
Reference: CodePipeline ECS Deployment
Create an AWS Lambda Function that Runs Connectivity Checks and API Calls against the Service.
Integrate the Lambda Function with CodePipeline by Using a Lambda Action Stage:
The Lambda function can perform the necessary integration tests by making connectivity checks and API calls to the deployed service endpoint.
Integrating this Lambda function into CodePipeline ensures that these tests are run automatically after deployment, providing near-real-time feedback on the new deployment's health.
Example Lambda function integration:
actions:
- name: TestService
actionTypeId:
category: Test
owner: AWS
provider: Lambda
version: 1
runOrder: 2
configuration:
FunctionName: testServiceFunction
Reference: Integrating Lambda with CodePipeline
These steps ensure that the CI/CD workflow deploys the new container image to ECS, updates the image references, and performs integration tests, meeting the requirements with minimal management overhead.
NEW QUESTION # 398
A company is developing a mobile app that requires extensive automated testing across multiple device types. The company is using AWS CodePipeline for its CI/CD pipeline. The company must implement a scalable testing solution that can handle increased test loads as the app grows. Which solution will meet these requirements with the LEAST management overhead?
Answer: C
Explanation:
AWS Device Farm provides a fully managed environment for automated and manual app testing across real physical devices. It integrates directly with AWS CodePipeline and automatically scales based on test concurrency. AWS documentation recommends Device Farm for mobile app testing due to its scalability and zero infrastructure management.
NEW QUESTION # 399
A company deploys a web application on Amazon EC2 instances that are behind an Application Load Balancer (ALB). The company stores the application code in an AWS CodeCommit repository. When code is merged to the main branch, an AWS Lambda function invokes an AWS CodeBuild project. The CodeBuild project packages the code, stores the packaged code in AWS CodeArtifact, and invokes AWS Systems Manager Run Command to deploy the packaged code to the EC2 instances.
Previous deployments have resulted in defects, EC2 instances that are not running the latest version of the packaged code, and inconsistencies between instances.
Which combination of actions should a DevOps engineer take to implement a more reliable deployment solution? (Select TWO.)
Answer: A,B
Explanation:
To implement a more reliable deployment solution, a DevOps engineer should take the following actions:
Create a pipeline in AWS CodePipeline that uses the CodeCommit repository as a source provider. Configure pipeline stages that run the CodeBuild project in parallel to build and test the application. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action. This action will improve the deployment reliability by automating the entire process from code commit to deployment, reducing human errors and inconsistencies. By running the build and test stages in parallel, the pipeline can also speed up the delivery time and provide faster feedback. By using CodeDeploy as the deployment action, the pipeline can leverage the features of CodeDeploy, such as traffic shifting, health checks, rollback, and deployment configuration123 Create an AWS CodeDeploy application and a deployment group to deploy the packaged code to the EC2 instances. Configure the ALB for the deployment group. This action will improve the deployment reliability by using CodeDeploy to orchestrate the deployment across multiple EC2 instances behind an ALB. CodeDeploy can perform blue/green deployments or in-place deployments with traffic shifting, which can minimize downtime and reduce risks. CodeDeploy can also monitor the health of the instances during and after the deployment, and automatically roll back if any issues are detected. By configuring the ALB for the deployment group, CodeDeploy can register and deregister instances from the load balancer as needed, ensuring that only healthy instances receive traffic45 The other options are not correct because they do not improve the deployment reliability or follow best practices. Creating separate pipeline stages that run a CodeBuild project to build and then test the application is not a good option because it will increase the pipeline execution time and delay the feedback loop. Creating individual Lambda functions that use CodeDeploy instead of Systems Manager to run build, test, and deploy actions is not a valid option because it will add unnecessary complexity and cost to the solution. Lambda functions are not designed for long-running tasks such as building or deploying applications. Creating an Amazon S3 bucket and modifying the CodeBuild project to store the packages in the S3 bucket instead of in CodeArtifact is not a necessary option because it will not affect the deployment reliability. CodeArtifact is a secure, scalable, and cost-effective package management service that can store and share software packages for application development67
:
1: What is AWS CodePipeline? - AWS CodePipeline
2: Create a pipeline in AWS CodePipeline - AWS CodePipeline
3: Deploy an application with AWS CodeDeploy - AWS CodePipeline
4: What is AWS CodeDeploy? - AWS CodeDeploy
5: Configure an Application Load Balancer for your blue/green deployments - AWS CodeDeploy
6: What is AWS Lambda? - AWS Lambda
7: What is AWS CodeArtifact? - AWS CodeArtifact
NEW QUESTION # 400
A company needs to update its order processing application to improve resilience and availability. The application requires a stateful database and uses a single-node Amazon RDS DB instance to store customer orders and transaction history. A DevOps engineer must make the database highly available.
Which solution will meet this requirement?
Answer: C
NEW QUESTION # 401
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