AWS DOP-C02 Free Practice Questions — Page 1

Correct Answer: A. Modify the Lambda function to write the API operation name, response code, and version number as a log line to an Amazon CloudWatch Logs log group. Con gure a CloudWatch Logs metric lter that increments a metric for each API operation name. Specify response code and application version as dimensions for the metric.

Why A is correct: CloudWatch Logs metric filters are the appropriate solution for extracting custom metrics from log data. When the Lambda function writes structured log lines containing the API operation name, response code, and version number, a metric filter can parse these logs and increment CloudWatch metrics. The dimensions (response code and application version) allow for granular tracking of each combination, which is exactly what's needed to monitor different app versions and their response codes per operation.Why other options are wrong: B: CloudWatch Logs Insights is a query tool for ad-hoc analysis, not for automatically populating metrics. It cannot create real-time metrics with dimensions. C: ALB access logs don't capture custom application data like API operation names or user-agent header details that the Lambda function extracts. Response metadata from Lambda isn't logged in ALB access logs. D: X-Ray is designed for distributed tracing and request analysis, not for creating custom CloudWatch metrics with specific dimensions. While X-Ray can track subsegments, it doesn't natively publish custom metrics to CloudWatch in the way described.

Correct Answer: C. Con gure provisioned concurrency on the Lambda function. Con gure AWS Application Auto Scaling on the Lambda function with provisioned concurrency values set to a minimum of 1 and a maximum of 100.

Why C is correct: Provisioned concurrency keeps Lambda functions initialized and ready to respond immediately, eliminating cold starts. AWS Application Auto Scaling can dynamically adjust the provisioned concurrency based on demand, handling the 10x traffic spike during midday while scaling down to minimum capacity (1) during low-traffic periods. This addresses both the cold-start latency issue and the variable traffic patterns efficiently. Why other options are wrong: A: A concurrency value of 1 is insufficient for thousands of requests, especially during the 10x spike. Deleting DAX is counterproductive as it helps with DynamoDB read performance. B: Reserved concurrency of 0 would prevent the function from executing at all—it caps maximum concurrent executions but doesn't eliminate cold starts. D: Reserved concurrency doesn't address cold starts; it only limits maximum concurrent executions. Auto Scaling on API Gateway is not the solution since the bottleneck is the Lambda cold-start time, not API Gateway capacity.

Correct Answer: B. Create a script that uses the CodeDeploy environment variable DEPLOYMENT_GROUP_ NAME to identify which deployment group the instance is part of. Use this information to con gure the log level settings. Reference this script as part of the BeforeInstall lifecycle hook in the appspec.yml le.

Why B is correct: The DEPLOYMENT_GROUP_NAME environment variable is automatically provided by CodeDeploy to all deployment scripts. This allows the script to dynamically determine which deployment group it's running in without any additional configuration. The BeforeInstall lifecycle hook runs before the application is installed, making it the appropriate time to configure log levels. Since the environment variable is built-in, this approach requires no management overhead and works across all deployment groups without modification. Why other options are wrong: A: Using EC2 tags and API calls adds unnecessary complexity and requires managing IAM permissions. AfterInstall runs after installation, which is too late to configure settings needed during deployment. C: CodeDeploy doesn't support custom environment variables in the way described. ValidateService runs after deployment to verify the service is working, which is too late for configuration. D: DEPLOYMENT_GROUP_ID is a GUID that doesn't directly indicate environment type. The Install lifecycle hook is for the actual installation, not for configuration that should happen before installation.

Correct Answer: B. Set up AWS Con g in the account. Use a managed rule that returns a compliance failure for EC2::Volume resources that do not have a Backup Frequency tag applied. Con gure a remediation action that uses a custom AWS Systems Manager Automation runbook to apply the Backup_Frequency tag with a value of weekly.

Why B is correct: AWS Config's managed rule for EC2::Volume resources specifically checks for tag compliance on EBS volumes. When a volume lacks the Backup_Frequency tag, Config detects this as non-compliant and triggers an automatic remediation action. The Systems Manager Automation runbook can apply the default "weekly" tag. This is the most efficient solution because it uses a pre-built managed rule designed exactly for this purpose. Why other options are wrong: A: Creating a custom rule for "all Amazon EC2 resources" is overly broad and less efficient than using the managed rule that specifically targets EBS volumes (EC2::Volume). C & D: EventBridge with CloudTrail for CreateVolume events would only catch new volumes, but the problem states existing volumes are also missing tags. Additionally, ModifyVolume events don't necessarily indicate missing tags. This reactive approach is less comprehensive than Config's continuous compliance monitoring.

Correct Answer: A. Add a reader instance to the Aurora cluster. Update the application to use the Aurora cluster endpoint for write operations. Update the Aurora cluster's reader endpoint for reads.

Why A is correct: Adding a reader instance creates a read replica that can handle read traffic during maintenance. Using the cluster endpoint for writes ensures writes always go to the primary instance, while the reader endpoint automatically distributes read traffic across available readers. During maintenance, if the primary instance is updated, Aurora can promote the reader to primary with minimal downtime, and the endpoints automatically adjust. This provides the least interruption. Why other options are wrong: B: Custom ANY endpoints don't provide the same automatic failover capabilities during maintenance. They're typically used for specific use cases, not for high-availability during maintenance windows. C: Aurora doesn't have a "Multi-AZ option" in the same way as RDS for other databases. Aurora is inherently Multi-AZ with storage replicated across multiple AZs. Adding a reader instance is the correct approach. D: Same issue as B—custom ANY endpoints don't provide the optimal high-availability configuration for maintenance windows.