Amazon DOP-C02 Exam Dumps

The latency in processing and ingesting logs can be caused by several factors, such as the throughput of the Kinesis data stream, the concurrency of the Lambda function, and the configuration of the event source mapping. To reduce the latency, the following steps can be taken:

Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer.This will allow the Lambda function to receive records from the data stream with dedicated throughput of up to 2 MB per second per shard, independent of other consumers1. This will reduce the contention and delay in accessing the data stream.

Increase the ParallelizationFactor setting in the Lambda event source mapping.This will allow the Lambda service to invoke more instances of the function concurrently to process the records from the data stream2. This will increase the processing capacity and reduce the backlog of records in the data stream.

Configure reserved concurrency for the Lambda function that processes the logs.This will ensure that the function has enough concurrency available to handle the increased load from the data stream3. This will prevent the function from being throttled by the account-level concurrency limit.

The other options are not effective or may have negative impacts on the latency.Option D is not suitable because increasing the batch size in the Kinesis data stream will increase the amount of data that the Lambda function has to process in each invocation, which may increase the execution time and latency4. Option E is not advisable because turning off the ReportBatchItemFailures setting in the Lambda event source mapping will prevent the Lambda service from retrying the failed records, which may result in data loss. Option F is not necessary because increasing the number of shards in the Kinesis data stream will increase the throughput of the data stream, but it will not affect the processing speed of the Lambda function, which is the bottleneck in this scenario.

1: Using AWS Lambda with Amazon Kinesis Data Streams - AWS Lambda

2: AWS Lambda event source mappings - AWS Lambda

3: Managing concurrency for a Lambda function - AWS Lambda

4: AWS Lambda function scaling - AWS Lambda

: AWS Lambda event source mappings - AWS Lambda

: Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams

Step 2: Use an Application Load Balancer and Auto Scaling Group The Application Load Balancer (ALB) is essential to balance traffic across multiple instances, and Auto Scaling ensures the deployment scales automatically to meet demand.

Action: Associate the Auto Scaling group and Application Load Balancer target group with the deployment group.

Why: This configuration ensures that traffic is evenly distributed and that instances automatically scale based on traffic load.

Step 3: Use Custom Deployment Configuration The company requires that traffic be rerouted to at least half of the instances to succeed. AWS CodeDeploy allows you to configure custom deployment settings with specific thresholds for healthy hosts.

Action: Create a custom deployment configuration where 50% of the instances must be healthy.

Why: This ensures that the deployment continues only if at least 50% of the new instances are healthy.

Step 4: Clean Temporary Files Using Hooks Before routing traffic to the new environment, the temporary files generated during the deployment must be deleted. This can be achieved using the BeforeAllowTraffic hook in the appspec.yml file.

Action: Use the BeforeAllowTraffic lifecycle event hook to clean up temporary files before routing traffic to the new environment.

Why: This ensures that the environment is clean before the new instances start serving traffic.

Step 5: Terminate Original Instances After Deployment After a successful deployment, AWS CodeDeploy can automatically terminate the original instances (blue environment) to save costs.

Action: Instruct AWS CodeDeploy to terminate the original instances after the new instances are healthy.

Why: This helps in cost reduction by removing unused instances after the deployment.

This corresponds to Option C: Use an Application Load Balancer and a blue/green deployment. Associate the Auto Scaling group and the Application Load Balancer target group with the deployment group. Use the Automatically copy Auto Scaling group option, and use CodeDeployDefault.HalfAtATime as the deployment configuration. Instruct AWS CodeDeploy to terminate the original instances in the deployment group, and use the BeforeAllowTraffic hook within appspec.yml to delete the temporary files.

Amazon ECR enhanced scanning uses Amazon Inspector for vulnerability detection.

EventBridge can capture Inspector scan findings.

Lambda can process scan findings and reject manual approval if critical vulnerabilities exist.

Options A and C use incorrect or less integrated services (basic scanning or Detective).

Option B adds unnecessary complexity with SBOM and Athena.

Amazon ECR Image Scanning

Integrating ECR Scanning with CodePipeline

When AWS CodeDeploy performs an in-place deployment, it updates the instances with the new application revision one at a time, as specified by the deployment configurationcodeDeployDefault.oneAtATime. If a lifecycle event hook, such asAfterInstall, fails during the deployment, CodeDeploy will attempt to roll back to the previous version on the affected instances. This is likely what happened with the two instances that still have the previous application revision deployed. The failure of theAfterInstalllifecycle event hook triggered the rollback mechanism, resulting in those instances reverting to the previous application revision.

AWS CodeDeploy documentation on redeployment and rollback procedures1.

Stack Overflow discussions on re-deploying older revisions with AWS CodeDeploy2.

AWS CLI reference guide for deploying a revision2.