Free Amazon Web Services DOP-C02 Practice Exam with Questions & Answers | Set: 8

The company needs multi-Region data persistence with immediate propagation and minimal operational overhead. For S3, the correct mechanism is S3 replication (Cross-Region Replication or Same-Region Replication), which continuously and asynchronously replicates new objects as they are written. Configuring two-way replication between the primary and secondary Region buckets ensures that objects written in either Region are replicated to the other automatically without custom code.

For DynamoDB, the native solution for multi-Region replication is DynamoDB global tables. Global tables provide multi-master, multi-Region replication with low-latency reads and writes in each Region and automatic propagation of changes. Converting existing tables into global tables and adding the secondary Region as a replica gives immediate, managed cross-Region replication with minimal maintenance.

Option A combines these two fully managed features: two-way S3 replication and DynamoDB global tables. This yields the highest operational efficiency.

Options B, C, and D rely on S3 Batch Operations or DynamoDB Streams + Lambda to manually copy data cross-Region. These approaches add complexity, custom code, and operational risk, and they are less suitable when AWS provides managed replication mechanisms specifically designed for this purpose.

Therefore, Option A is the correct and most efficient solution.

Comprehensive and Detailed Explanation From Exact Extract:

Amazon ECR supports enhanced scanning powered by Amazon Inspector, which provides deeper security scanning for container images including OS and programming language package vulnerabilities.

Enabling enhanced scanning (Option B) allows detection of CRITICAL and HIGH vulnerabilities.

Amazon ECR emits scan completion events via EventBridge, which can trigger Lambda functions. The Lambda function can process the scan results from Amazon Inspector and programmatically approve or reject the image in the CI/CD pipeline (Option D).

Basic scanning (Option A) is limited and does not integrate with Inspector.

Options C and E describe functionalities not natively supported (ECR does not automatically submit Rejected status; Clair is not used in AWS ECR scanning).

To meet the requirements, the DevOps engineer needs to configure the CloudWatch alarm to stop the EC2 instances when the average of the NetworkPacketsIn metric is less than 5 for at least 3 hours in a 12-hour time window. This means that the alarm should trigger when 3 out of 12 datapoints are below the threshold of 5. The alarm should also treat missing data as not breaching the threshold, so that the EC2 instances continue to run if there is no data for the metric during the evaluation period. The DevOps engineer can add an EC2 action to stop the instance when the alarm enters the ALARM state, which is a built-in action type for CloudWatch alarms.

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.

This solution will meet the requirements because it will use a CloudFormation custom resource to execute custom logic during the stack set operation. A custom resource is a resource that you define in your template and that is associated with an AWS Lambda function. The Lambda function runs whenever the custom resource is created, updated, or deleted, and can perform any actions that are supported by the AWS SDK. In this case, the Lambda function can use the GuardDuty API to check whether GuardDuty is already enabled in each target account, and if not, enable it. This way, the DevOps engineer can avoid deploying the stack set to accounts that already have GuardDuty enabled, and prevent failure during the deployment.

These solutions will meet the requirement because they will prevent the loss of notification messages in the future. An Amazon SQS queue is a service that provides a reliable, scalable, and secure message queue for asynchronous communication between distributed components. You can use an SQS queue to buffer messages from an SNS topic and ensure that they are delivered and processed by a Lambda function, even if the function or the database is temporarily unavailable.

Option C will configure an SQS dead-letter queue for the SNS topic. A dead-letter queue is a queue that receives messages that could not be delivered to any subscriber after a specified number of retries. You can use a dead-letter queue to store and analyze failed messages, or to reprocess them later. This way, you can avoid losing messages that could not be delivered to the Lambda function due to network errors, throttling, or other issues.

Option D will subscribe an SQS queue to the SNS topic and configure the Lambda function to process messages from the SQS queue. This will decouple the SNS topic from the Lambda function and provide more flexibility and control over the message delivery and processing. You can use an SQS queue to store messages from the SNS topic until they are ready to be processed by the Lambda function, and also to retry processing in case of failures. This way, you can avoid losing messages that could not be processed by the Lambda function due to database errors, timeouts, or other issues.

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.

CodeDeploy supports TimeBasedLinear traffic shifting for ECS blue/green deployments. Traffic increments by linearPercentage every linearInterval until 100%. This provides zero-downtime gradual rollout — as per CodeDeploy ECS Blue/Green Traffic Shifting documentation.