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Amazon DOP-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Resilient Cloud Solutions	15%	- High availability and fault tolerance design 1. Disaster recovery strategies 2. Multi-AZ and multi-region architectures
SDLC Automation	22%	- CI/CD pipeline design and implementation 1. Build and deployment automation 2. Pipeline optimization and scaling
Configuration Management and Infrastructure as Code	17%	- Infrastructure provisioning and automation 1. Configuration tools and automation strategies 2. AWS CloudFormation and CDK usage
Security and Compliance Automation	13%	- Security automation in CI/CD and infrastructure 1. Compliance monitoring and auditing 2. IAM policy automation and governance
Incident and Event Management	18%	- Operational response and recovery 1. Automated event-driven responses 2. Incident detection and remediation
Monitoring and Logging	15%	- Observability and metrics 1. CloudWatch monitoring and alarms 2. Log aggregation and analysis

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q270-Q275):

NEW QUESTION # 270
A DevOps engineer is building an application that uses an AWS Lambda function to query an Amazon Aurora MySQL DB cluster. The Lambda function performs only read queries. Amazon EventBridge events invoke the Lambda function.
As more events invoke the Lambda function each second, the database's latency increases and the database's throughput decreases. The DevOps engineer needs to improve the performance of the application.
Which combination of steps will meet these requirements? (Select THREE.)

A. Implement the database connection opening and closing inside the Lambda event handler code.
B. Implement the database connection opening outside the Lambda event handler code.
C. Connect to the Aurora cluster endpoint from the Lambda function.
D. Use Amazon RDS Proxy to create a proxy. Connect the proxy to the Aurora cluster reader endpoint. Set a maximum connections percentage on the proxy.
E. Implement database connection pooling inside the Lambda code. Set a maximum number of connections on the database connection pool.
F. Connect to the proxy endpoint from the Lambda function.

Answer: B,D,F

Explanation:
Explanation
Short Explanation: To improve the performance of the application, the DevOps engineer should use Amazon RDS Proxy, implement the database connection opening outside the Lambda event handler code, and connect to the proxy endpoint from the Lambda function.
References:
Amazon RDS Proxy is a fully managed, highly available database proxy for Amazon Relational Database Service (RDS) that makes applications more scalable, more resilient to database failures, and more secure1. By using Amazon RDS Proxy, the DevOps engineer can reduce the overhead of opening and closing connections to the database, which can improve latency and throughput2.
The DevOps engineer should connect the proxy to the Aurora cluster reader endpoint, which allows read-only connections to one of the Aurora Replicas in the DB cluster3. This can help balance the load across multiple read replicas and improve performance for read-intensive workloads4.
The DevOps engineer should implement the database connection opening outside the Lambda event handler code, which means using a global variable to store the database connection object5. This can enable connection reuse across multiple invocations of the Lambda function, which can reduce latency and improve performance.
The DevOps engineer should connect to the proxy endpoint from the Lambda function, which is a unique URL that represents the proxy. This can allow the Lambda function to access the database through the proxy, which can provide benefits such as connection pooling, load balancing, failover handling, and enhanced security.
The other options are incorrect because:
Implementing database connection pooling inside the Lambda code is unnecessary and redundant when using Amazon RDS Proxy, which already provides connection pooling as a service.
Implementing the database connection opening and closing inside the Lambda event handler code is inefficient and costly, as it can increase latency and consume more resources for each invocation of the Lambda function.
Connecting to the Aurora cluster endpoint from the Lambda function is not optimal for read-only queries, as it can direct traffic to either the primary instance or one of the Aurora Replicas in the DB cluster. This can result in inconsistent performance and potential conflicts with write operations on the primary instance.

NEW QUESTION # 271
A company has an application that runs on Amazon EC2 instances in an Auto Scaling group. The application processes a high volume of messages from an Amazon Simple Queue Service (Amazon SQS) queue.
A DevOps engineer noticed that the application took several hours to process a group of messages from the SQS queue. The average CPU utilization of the Auto Scaling group did not cross the threshold of a target tracking scaling policy when processing the messages. The application that processes the SQS queue publishes logs to Amazon CloudWatch Logs.
The DevOps engineer needs to ensure that the queue is processed quickly.
Which solution meets these requirements with the LEAST operational overhead?

A. Create an AWS Lambda function. Configure the Lambda function to publish a custom metric by using the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to publish the queue messages for each instance. Schedule an Amazon EventBridge rule to run the Lambda function every hour. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
B. Create an AWS Lambda function. Configure the Lambda function to publish a custom metric by using the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to publish the queue messages for each instance. Create a CloudWatch subscription filter for the application logs with the Lambda function as the target. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
C. Create an AWS Lambda function that logs the ApproximateNumberOfMessagesVisible attribute of the SQS queue to a CloudWatch Logs log group. Schedule an Amazon EventBridge rule to run the Lambda function every 5 minutes. Create a metric filter to count the number of log events from a CloudWatch logs group. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
D. Create a target tracking scaling policy for the Auto Scaling group. In the target tracking policy, use the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to calculate how many messages are in the queue for each number of instances by using metric math. Use the calculated attribute to scale in and out.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The default CPU utilization metric does not reflect the processing backlog in the SQS queue, so the Auto Scaling group is not scaling properly to handle the workload.
To scale the Auto Scaling group based on queue length, you can create a target tracking scaling policy that uses a custom metric that combines the SQS queue's ApproximateNumberOfMessagesVisible and the number of instances (GroupIn-ServiceInstances) metric using CloudWatch metric math. This allows the scaling policy to calculate the average number of messages per instance and scale accordingly.
This approach requires no additional Lambda functions or log processing, thus minimizing operational overhead.
Option A and B require Lambda functions to publish custom metrics, which increases operational complexity. Option D also adds complexity with logging and metric filters.
Reference:
Scaling based on SQS queue length using metric math:
"You can create CloudWatch metric math expressions combining SQS and Auto Scaling group metrics to enable target tracking scaling policies that respond to queue backlog." (AWS Auto Scaling with SQS) Target Tracking Scaling Policies:
"Target tracking policies can use metric math expressions as a source to make scaling decisions." (AWS Auto Scaling Target Tracking)

NEW QUESTION # 272
A company wants to set up a continuous delivery pipeline. The company stores application code in a private GitHub repository. The company needs to deploy the application components to Amazon Elastic Container Service (Amazon ECS). Amazon EC2, and AWS Lambda. The pipeline must support manual approval actions.
Which solution will meet these requirements?

A. Use AWS CodeDeploy with GitHub integration to deploy the application.
B. Use AWS CodePipeline with AWS Elastic Beanstalk as the deploy provider.
C. Use AWS CodePipeline with Amazon ECS. Amazon EC2, and Lambda as deploy providers.
D. Use AWS CodePipeline with AWS CodeDeploy as the deploy provider.

Answer: D

Explanation:
Explanation
https://docs.aws.amazon.com/codedeploy/latest/userguide/deployment-steps.html

NEW QUESTION # 273
A company builds a container image in an AWS CodeBuild project by running Docker commands. After the container image is built, the CodeBuild project uploads the container image to an Amazon S3 bucket. The CodeBuild project has an 1AM service role that has permissions to access the S3 bucket.
A DevOps engineer needs to replace the S3 bucket with an Amazon Elastic Container Registry (Amazon ECR) repository to store the container images. The DevOps engineer creates an ECR private image repository in the same AWS Region of the CodeBuild project. The DevOps engineer adjusts the 1AM service role with the permissions that are necessary to work with the new ECR repository. The DevOps engineer also places new repository information into the docker build command and the docker push command that are used in the buildspec.yml file.
When the CodeBuild project runs a build job, the job fails when the job tries to access the ECR repository.
Which solution will resolve the issue of failed access to the ECR repository?

A. Update the buildspec.yml file to use the AWS CLI to assume the 1AM service role for ECR operations. Add an ECR repository policy that allows the 1AM service role to have access.
B. Add an environment variable of type SECRETS_MANAGER to the CodeBuild project. In the environment variable, include the ARN of the CodeBuild project's lAM service role. Update the buildspec.yml file to use the new environment variable to log in with the docker login command to access the ECR repository.
C. Update the buildspec.yml file to log in to the ECR repository by using the aws ecr get-login-password AWS CLI command to obtain an authentication token. Update the docker login command to use the authentication token to access the ECR repository.
D. Update the ECR repository to be a public image repository. Add an ECR repository policy that allows the 1AM service role to have access.

Answer: C

NEW QUESTION # 274
A company is building a web and mobile application that uses a serverless architecture powered by AWS Lambda and Amazon API Gateway The company wants to fully automate the backend Lambda deployment based on code that is pushed to the appropriate environment branch in an AWS CodeCommit repository The deployment must have the following:
* Separate environment pipelines for testing and production
* Automatic deployment that occurs for test environments only
Which steps should be taken to meet these requirements'?

A. Configure a new AWS CodePipelme service Create a CodeCommit repository for each environment Set up CodePipeline to retrieve the source code from the appropriate repository Set up the deployment step to deploy the Lambda functions with AWS CloudFormation.
B. Create two AWS CodePipeline configurations for test and production environments Configure the production pipeline to have a manual approval step. Create one CodeCommit repository with a branch for each environment Set up each CodePipeline to retrieve the source code from the appropriate branch in the repository. Set up the deployment step to deploy the Lambda functions with AWS CloudFormation
C. Create two AWS CodePipeline configurations for test and production environments Configure the production pipeline to have a manual approval step Create aCodeCommit repository for each environment Set up each CodePipeline to retrieve the source code from the appropriate repository Set up the deployment step to deploy the Lambda functions with AWS CloudFormation.
D. Create an AWS CodeBuild configuration for test and production environments Configure the production pipeline to have a manual approval step. Create one CodeCommit repository with a branch for each environment Push the Lambda function code to an Amazon S3 bucket Set up the deployment step to deploy the Lambda functions from the S3 bucket.

Answer: B

Explanation:
The correct approach to meet the requirements for separate environment pipelines and automatic deployment for test environments is to create two AWS CodePipeline configurations, one for each environment. The production pipeline should have a manual approval step to ensure that changes are reviewed before being deployed to production. A single AWS CodeCommit repository with separate branches for each environment allows for organized and efficient code management. Each CodePipeline retrieves the source code from the appropriate branch in the repository. The deployment step utilizes AWS CloudFormation to deploy the Lambda functions, ensuring that the infrastructure as code is maintained and version-controlled.
AWS Lambda with Amazon API Gateway: Using AWS Lambda with Amazon API Gateway Tutorial on using Lambda with API Gateway: Tutorial: Using Lambda with API Gateway AWS CodePipeline automatic deployment: Set Up a Continuous Deployment Pipeline Using AWS CodePipeline Building a pipeline for test and production stacks: Walkthrough: Building a pipeline for test and production stacks

NEW QUESTION # 275
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