DOP-C02 Exam Dumps : AWS Certified DevOps Engineer - Professional

Amazon CloudWatch metric streams are designed to deliver metrics in near real time to downstream services like Amazon Data Firehose with minimal configuration and management overhead. By configuring a metric stream to include all metrics, you ensure that any newly created performance or custom metrics are automatically included without needing to update filters or code when namespaces or metrics change.

Option B uses a CloudWatch metric stream → Firehose → Lambda → S3 pattern. Firehose natively supports invoking a Lambda function for in-flight transformation of the metric data before final delivery to S3. This satisfies the requirement to transform metrics before storing them and provides a fully managed, serverless pipeline with very little ongoing operational burden.

Option A mentions including metrics from “the application and the CloudWatch namespace,” which implies more manual selection and maintenance, potentially missing new metrics if not explicitly added. Options C and D work at the log level, not at the metric level, and would require building additional logic to derive metrics from logs. This is more complex and not aligned with “least operational overhead.”

Therefore, Option B is the most efficient and scalable solution.

When you create a pipeline from CodePipeline during the step-by-step it creates a CloudWatch Event rule for a given branch and repo

like this:

{

"source": [

"aws.codecommit"

],

"detail-type": [

"CodeCommit Repository State Change"

],

"resources": [

"arn:aws:codecommit:us-east-1:xxxxx:repo-name"

],

"detail": {

"event": [

"referenceCreated",

"referenceUpdated"

],

"referenceType": [

"branch"

],

"referenceName": [

"master"

]

}

}

To meet the requirements of failover and disaster recovery, the company should use the following deployment strategies:

Create an Amazon Aurora global database in two AWS Regions as the data store. In the event of a failure, promote the secondary Region to the primary for the application. Update the application to use the Aurora cluster endpoint in the secondary Region. This strategy can provide a low RPO and RTO for the data, as Aurora global database replicates data with minimal latency across Regions and allows fast and easy failover12. The company can use the Amazon Aurora cluster endpoint to connect to the current primary DB cluster without needing to change any application code1.

Set up the application in two AWS Regions. Configure AWS Global Accelerator to point to Application Load Balancers (ALBs) in both Regions. Add both ALBs to a single endpoint group. Use health checks and Auto Scaling groups in each Region. This strategy can provide high availability and performance for the application, as AWS Global Accelerator uses the AWS global network to route traffic to the closest healthy endpoint3. The company can also use static IP addresses that are assigned by Global Accelerator as a fixed entry point for their application1. By using health checks and Auto Scaling groups, the company can ensure that their application can scale up or down based on demand and handle any instance failures4.

The other options are incorrect because:

Creating an Amazon Aurora Single-AZ cluster in multiple AWS Regions as the data store would not provide a fast failover or disaster recovery solution, as the company would need to manually restore data from backups or snapshots in another Region in case of a failure.

Creating an Amazon Aurora cluster in multiple AWS Regions as the data store and using a Network Load Balancer to balance the database traffic in different Regions would not work, as Network Load Balancers do not support cross-Region routing. Moreover, this strategy would not provide a consistent view of the data across Regions, as Aurora clusters do not replicate data automatically between Regions unless they are part of a global database.

Setting up the application in two AWS Regions and using Amazon Route 53 failover routing that points to Application Load Balancers in both Regions would not provide a low RTO, as Route 53 failover routing relies on DNS resolution, which can take time to propagate changes across different DNS servers and clients. Moreover, this strategy would not provide deterministic routing, as Route 53 failover routing depends on DNS caching behavior, which can vary depending on different factors.