Free Amazon Web Services DOP-C02 Practice Exam with Questions & Answers | Set: 11

This failure is typically due to incorrectly configured health checks in Elastic Load Balancing for the Classic Load Balancer, Application Load Balancer, or Network Load Balancer used to manage traffic for the deployment group. To resolve the issue, review and correct any errors in the health check configuration for the load balancer. https://docs.aws.amazon.com/codedeploy/latest/userguide/troubleshooting-deployments.html#troubleshooting-deployments-allowtraffic-no-logs

 Create an AWS Identity and Access Management Roles Anywhere trust anchor Create an IAM role that allows CodeArtifact actions and that has a trust relationship on the trust anchor. Update the on-premises CI/CD pipeline to assume the new IAM role and to publish the packages to CodeArtifact:

Roles Anywhere allows on-premises servers to assume IAM roles, making it easier to integrate on-premises environments with AWS services.

Steps:

Create a trust anchor in IAM.

Create an IAM role with permissions for CodeArtifact actions (e.g., publishing packages).

Update the CI/CD pipeline to assume this role using the trust anchor.

 Create a new Amazon S3 bucket. Generate a presigned URL that allows the PutObject request. Update the on-premises CI/CD pipeline to use the presigned URL to publish the packages from the on-premises location to the S3 bucket. Create an AWS Lambda function that runs when packages are created in the bucket through a put command Configure the Lambda function to publish the packages to CodeArtifact:

Using an S3 bucket as an intermediary, you can easily upload packages from on-premises systems.

Steps:

Create an S3 bucket.

Generate presigned URLs to allow the CI/CD pipeline to upload packages.

Configure an AWS Lambda function to trigger on S3 PUT events and publish the packages to CodeArtifact.

This solution will meet the requirements in the most operationally efficient manner because it will use CloudWatch Logs destination to aggregate the log groups from all the accounts to a single S3 bucket in the logging account. However, unlike option A, this solution will create a CloudWatch Logs destination for each region, instead of a single destination for all regions. This will improve the performance and reliability of the log delivery, as it will avoid cross-region data transfer and latency issues. Moreover, this solution will use an Amazon Kinesis data stream and an Amazon Kinesis Data Firehose delivery stream for each region, instead of a single stream for all regions. This will also improve the scalability and throughput of the log delivery, as it will avoid bottlenecks and throttling issues that may occur with a single stream.

Verified Answer: A, C, and E.

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.

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.

Option A is correct because using batch writes to write multiple log events in a single write operation is a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Batch writes can reduce the number of network round trips and API calls, and can also take advantage of parallel processing by Timestream. Batch writes can also improve the compression ratio of data in the memory store and the magnetic store, which can reduce the storage costs and improve the query performance1.

Option B is incorrect because writing each log event as a single write operation is not a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Writing each log event as a single write operation would increase the number of network round trips and API calls, and would also reduce the compression ratio of data in the memory store and the magnetic store. This would increase the storage costs and degrade the query performance1.

Option C is incorrect because treating each log as a single-measure record is not a recommended practice for optimizing the query performance in Timestream. Treating each log as a single-measure record would result in creating multiple records for each timestamp, which would increase the storage size and the query latency. Moreover, treating each log as a single-measure record would require using joins to query multiple measures for the same timestamp, which would add complexity and overhead to the query processing2.

Option D is correct because treating each log as a multi-measure record is a recommended practice for optimizing the query performance in Timestream. Treating each log as a multi-measure record would result in creating a single record for each timestamp, which would reduce the storage size and the query latency. Moreover, treating each log as a multi-measure record would allow querying multiple measures for the same timestamp without using joins, which would simplify and speed up the query processing2.

Option E is incorrect because configuring the memory store retention period to be longer than the magnetic store retention period is not a valid option in Timestream. The memory store retention period must always be shorter than or equal to the magnetic store retention period. This ensures that data is moved from the memory store to the magnetic store before it expires out of the memory store3.

Option F is correct because configuring the memory store retention period to be shorter than the magnetic store retention period is a valid option in Timestream. The memory store retention period determines how long data is kept in the memory store, which is optimized for fast point-in-time queries. The magnetic store retention period determines how long data is kept in the magnetic store, which is optimized for fast analytical queries. By configuring these retention periods appropriately, you can balance your storage costs and query performance according to your application needs3.

https://docs.aws.amazon.com/autoscaling/ec2/userguide/ASGettingNotifications.html#auto-scaling-sns-notifications

Mounting EFS to EC2-backed EKS nodes requires:

NFS (port 2049) open from nodes to EFS (Security Group rule).

Mount targets in each subnet/AZ where nodes reside.

IAM role for the EFS CSI driver with elasticfilesystem:ClientMount and ClientRootAccess permissions. These are the standard setup requirements in “Using the Amazon EFS CSI Driver with Amazon EKS.”

The correct answer is B. Creating a new AWS CodeBuild project and configuring a test stage in the AWS CodePipeline pipeline that uses the new CodeBuild project is the best way to integrate the unit tests into the existing pipeline. Creating a CodeBuild report group and uploading the test reports to the new CodeBuild report group will produce reports about the unit tests for the company to view. Using JUNITXML as the output format for the unit tests is supported by CodeBuild and will generate a valid report.

Option A is incorrect because Amazon CodeGuru Reviewer is a service that provides automated code reviews and recommendations for improving code quality and performance. It is not a tool for running unit tests or producing test reports. Therefore, option A will not meet the requirements.

Option C is incorrect because AWS CodeArtifact is a service that provides secure, scalable, and cost-effective artifact management for software development. It is not a tool for running unit tests or producing test reports. Moreover, option C uses CUCUMBERJSON as the output format for the unit tests, which is not supported by CodeBuild and will not generate a valid report.

Option D is incorrect because uploading the test reports to an Amazon S3 bucket is not the best way to produce reports about the unit tests for the company to view. CodeBuild has a built-in feature to create and manage test reports, which is more convenient and efficient than using S3. Furthermore, option D uses HTML as the output format for the unit tests, which is not supported by CodeBuild and will not generate a valid report.

AWS Config offers a managed rule called CLOUDFORMATION_STACK_DRIFT_DETECTION_CHECK to detect stack drift automatically.

Creating an SNS topic and subscribing the DevOps lead enables simple notification without custom Lambda functions.

An EventBridge rule that triggers on NON_COMPLIANT status (when drift is detected) can send alerts directly to SNS. Option B adds complexity by requiring custom rules and Lambda. Option C incorrectly triggers on COMPLIANT, not NON_COMPLIANT. Option D needs a Lambda for email, while SNS can send email directly. Hence, Option A offers the least operational effort.

To aggregate events from multiple accounts into a single account, the default event bus in the receiving (DevOps) account must have a resource-based policy allowing the source accounts to put events into it.

Then, an EventBridge rule in each source account routes Amazon Connect events to the default event bus in the DevOps account (cross-account event delivery).

Options A and B describe policies or rules incorrectly applying permissions or routing. Option D mentions replay permissions, which are unrelated to event routing.