DVA-C02 Development with AWS Services Practice Test 17 — 15 Questions

Question 1

A company runs a microservices architecture on Amazon ECS with Fargate. Each service uses an Application Load Balancer and stores data in Amazon DynamoDB. The operations team notices that during traffic spikes, some requests fail with HTTP 503 errors. CloudWatch metrics show that the ALB's TargetResponseTime is increasing, and the DynamoDB table's ConsumedWriteCapacityUnits are reaching the provisioned limit. The team wants to handle traffic spikes gracefully without manual intervention. What should they do?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Enable DynamoDB Auto Scaling for write capacity and configure ECS Service Auto Scaling based on ALB request count.. DynamoDB Auto Scaling automatically adjusts the provisioned write capacity based on traffic, preventing throttling and 503 errors from write capacity exhaustion. ECS Service Auto Scaling adds more tasks when the ALB request count increases, distributing the load. Together, they handle traffic spikes without manual intervention. Option A is wrong because manually increasing provisioned capacity does not scale automatically, and deregistration delay does not address capacity issues. Option B is wrong because while SQS can buffer write requests, it adds complexity and latency; the question asks for graceful handling, and auto scaling addresses the root cause more directly. Option C is wrong because DAX is a cache for read operations, not write capacity.

Answer

Increase the DynamoDB table's provisioned write capacity and the ALB's target group deregistration delay.

Answer

Use an SQS queue to buffer write requests and process them asynchronously.

Answer

Add a DynamoDB Accelerator (DAX) cluster to cache frequently accessed data.

Question 2

A developer is deploying a web application using AWS Elastic Beanstalk. The application requires a relational database. The developer wants the database to be automatically created and configured as part of the Elastic Beanstalk environment. Which approach should they use?

Accepted Answer

Configure the Elastic Beanstalk environment to include an RDS database instance.. Elastic Beanstalk can provision an RDS database as part of the environment using the 'Add Database' configuration or via .ebextensions. This automatically creates and manages the RDS instance, linking it to the environment. Option A is incorrect because DynamoDB is a NoSQL database, not a relational one. Option B is incorrect because manually creating an RDS instance outside of Elastic Beanstalk does not integrate with environment lifecycles or automated management. Option C is incorrect because embedding a SQLite database file is not suitable for production due to scalability and reliability issues.

Answer

Use Amazon DynamoDB as the database and configure it in the Elastic Beanstalk environment.

Answer

Create an RDS database manually and configure the application to connect to it using environment properties.

Answer

Embed a SQLite database file in the application deployment package.

Question 3

A company is using AWS CodePipeline to automate deployments of a serverless application. The pipeline has a source stage (CodeCommit), a build stage (CodeBuild), and a deploy stage (CloudFormation). The deploy stage fails intermittently with the error 'Rate exceeded' when creating or updating stacks. What should the developer do to resolve this issue?

Accepted Answer

Request a service limit increase for CloudFormation API calls.. The 'Rate exceeded' error indicates that the AWS CloudFormation API throttling limits have been exceeded. This typically happens when multiple pipelines or processes are making concurrent calls to CloudFormation (such as CreateStack, UpdateStack, or DescribeStacks). Because the CloudFormation deployment action in CodePipeline is a managed action, developers cannot directly configure custom retry logic or exponential backoff parameters for it. To resolve this intermittent throttling issue, the developer should request a service quota (limit) increase for the CloudFormation API throttling limits from AWS Support.

Answer

Use the AWS CLI to deploy the CloudFormation stack instead of CodePipeline.

Answer

Increase the timeout for the CloudFormation deployment action in the pipeline.

Answer

Add a retry configuration with exponential backoff to the CloudFormation deployment action.

Question 4

A developer is creating a CI/CD pipeline using AWS CodeBuild and AWS CodeDeploy for a Java application that runs on EC2 instances. The build process must compile the code, run unit tests, and package the application into a WAR file. The deployment should use the blue/green deployment strategy. What is the correct sequence of actions?

Accepted Answer

Use CodeCommit to store the source code, CodeBuild to build, and CodePipeline to orchestrate the deployment.. CodeCommit stores source code, CodeBuild builds the application (compile, test, package into WAR), and CodePipeline orchestrates the entire pipeline including source, build, and deployment stages. CodeDeploy is used as the deployment provider within CodePipeline to perform blue/green deployments. Option A is correct because it describes the full CI/CD pipeline. Option D is incorrect because it omits the source stage and the orchestration layer provided by CodePipeline, which is essential for a complete CI/CD pipeline. Option B is incorrect because CodeDeploy is a deployment service, not a build service. Option C is incorrect because Elastic Beanstalk is a PaaS that abstracts the CI/CD process, not the direct use of CodeBuild and CodeDeploy.

Answer

Use CodeDeploy to build the application and then use CodePipeline to deploy it.

Answer

Use Elastic Beanstalk to build and deploy the application.

Answer

Use CodeBuild to compile and package the application, and then use CodeDeploy to perform the blue/green deployment.

Question 5

A developer is implementing a CI/CD pipeline using AWS CodePipeline. The pipeline has a source stage that uses an Amazon S3 bucket, a build stage that uses AWS CodeBuild, and a deploy stage that uses AWS CodeDeploy. The developer wants to ensure that the pipeline automatically triggers when a new file is uploaded to the S3 source bucket. Which TWO steps should the developer take to configure this? (Choose two.)

Accepted Answer

Enable S3 event notifications on the bucket to publish events to Amazon CloudWatch Events.. S3 event notifications can be sent to Amazon CloudWatch Events (now Amazon EventBridge), which can then trigger a CodePipeline execution. This is a supported integration pattern for detecting changes in an S3 source bucket. Option E is also correct because CodePipeline's built-in 'S3 source change detection' feature uses Amazon CloudWatch Events to automatically detect new or updated objects in the specified S3 bucket and start the pipeline.

Answer

Create a webhook in CodePipeline and configure S3 to send HTTP requests to the webhook URL.

Answer

Configure AWS CloudTrail to log S3 PutObject events and trigger the pipeline.

Answer

Create an AWS Lambda function that is invoked on S3 object creation and starts the pipeline.

Question 6

A developer is building a RESTful API using AWS Lambda and Amazon API Gateway. The API will be accessed by external customers. The developer needs to implement authentication and authorization. Which THREE steps should the developer take to secure the API? (Choose three.)

Accepted Answer

Use Amazon Cognito user pools for user authentication and to generate JWT tokens.. Amazon Cognito user pools provide a fully managed service for user authentication, allowing users to sign in and receive JSON Web Tokens (JWT). These tokens can then be used to authorize API requests, integrating directly with API Gateway as a built-in authorizer to secure the RESTful API.

Answer

Configure the API to use AWS IAM roles for authentication by passing the role ARN in the request.

Answer

Attach a resource policy to the API Gateway that allows only specific IAM users.

Question 7

A developer is deploying a new version of a microservice that runs on AWS Fargate. The service is part of an Amazon ECS cluster and has an associated Application Load Balancer (ALB). The developer wants to perform a rolling update without downtime and ensure that at least 50% of the service's desired count remains available during the deployment. The current desired count is 4. The developer updates the task definition and triggers a new service deployment using the AWS CLI. After the update, the developer notices that the service briefly goes to 0 running tasks during the deployment, causing downtime. The ECS service deployment configuration has: minimumHealthyPercent=50 and maximumPercent=200. What is the most likely cause of this downtime?

Accepted Answer

The service is using the CODE_DEPLOY deployment controller instead of the default ECS rolling update controller.. The deployment controller default is ECS, not CODE_DEPLOY. ECS rolling update with min/max settings should work, but if the deployment controller is set to 'CODE_DEPLOY', the service does not manage the rollout automatically; it expects an external deployment. In this case, the developer triggered a new deployment with the updated task definition but the service might have been configured with CODE_DEPLOY controller, causing it to stop all tasks. Option A is incorrect because 50% minimum healthy percent should keep at least 2 tasks. Option C is incorrect because deregistration delay affects draining, not immediate 0 count. Option D is incorrect because the ALB health check doesn't cause tasks to stop.

Answer

The ALB health check is misconfigured, causing healthy tasks to be marked as unhealthy and replaced.

Answer

The minimumHealthyPercent value of 50% is too low, allowing the service to scale down to 0 tasks.

Answer

The ALB deregistration delay is set to 0, causing tasks to be removed immediately.

Question 8

A company runs a Node.js application on AWS Elastic Beanstalk. The application writes log files to /var/log/app/. The operations team wants to stream these logs to Amazon CloudWatch Logs for monitoring and alerting. The developer configures the Elastic Beanstalk environment to include a .ebextensions configuration file that sets up the CloudWatch Logs agent. The configuration file specifies the log group and the log stream prefix. After deploying the updated environment, the logs are not appearing in CloudWatch Logs. The developer checks the EC2 instance and confirms that the CloudWatch Logs agent is running and the configuration file is present in /etc/awslogs/. What is the most likely reason the logs are not being sent?

Accepted Answer

The CloudWatch Logs agent configuration file does not specify the correct log file path or the log files do not exist.. The most likely reason logs are not appearing is that the CloudWatch Logs agent configuration file (usually at /etc/awslogs/awslogs.conf) does not specify the correct log file path or the log files do not exist. The agent runs as root, so permissions are typically not an issue. The .ebextensions configuration is valid and would be executed. While IAM permissions are necessary, the agent itself will not send logs if it cannot find the files to read, making incorrect path the primary suspect.

Answer

The CloudWatch Logs agent does not have read permissions on the /var/log/app/ directory.

Answer

The .ebextensions configuration file is not executed because it is in the wrong directory.

Answer

The IAM instance profile does not have the necessary permissions to write to CloudWatch Logs.

Question 9

A developer is building a serverless application using AWS Lambda that processes messages from an Amazon SQS queue. The queue receives about 100 messages per second, and each message takes about 30 seconds to process. The Lambda function is configured with a reserved concurrency of 10. The developer notices that messages are frequently being sent to the dead-letter queue (DLQ) after three failed processing attempts. The Lambda function's execution role has the necessary permissions to read from the SQS queue and write to the DLQ. The SQS queue's visibility timeout is set to 60 seconds, and the Lambda function's timeout is set to 60 seconds. What is the most likely cause of the messages being sent to the DLQ?

Accepted Answer

The reserved concurrency of 10 is too low to handle the incoming message rate, causing messages to be repeatedly retried until they exceed the maxReceiveCount.. With a reserved concurrency of 10, the Lambda function can process at most 10 messages concurrently. Since each message takes 30 seconds, the maximum throughput is about 10 messages per 30 seconds = ~0.33 messages per second, far below the incoming rate of 100 messages per second. Messages that are not processed will become visible again after the visibility timeout (60 seconds). They will be retried, but due to the low concurrency, they will likely fail again, eventually exceeding the maxReceiveCount (default 3) and being sent to the DLQ. Option A is incorrect because long polling reduces empty responses but does not address the throughput limitation. Option C is incorrect because the 60-second timeout is sufficient for 30-second processing. Option D is incorrect because the DLQ triggers after the maxReceiveCount, not after the first attempt.

Answer

The SQS queue is not configured to use long polling, causing the Lambda function to receive empty responses and waste time.

Answer

The Lambda function timeout is too short for the processing time required.

Answer

The DLQ is incorrectly configured to receive all failed messages after the first attempt.

Question 10

A company runs a web application on Amazon EC2 instances behind an Application Load Balancer (ALB). The application stores user session data in an Amazon ElastiCache for Redis cluster. Recently, users have been experiencing intermittent session timeouts and data loss. The developer examines the application logs and finds errors indicating that the Redis cluster is returning 'READONLY You can't write against a read-only replica.' The ElastiCache cluster is configured as a Redis replication group with one primary and two replicas. The application's connection code uses the primary endpoint. What is the most likely cause of this issue?

Accepted Answer

A failover event occurred, and the application is still trying to write to the old primary node, which is now a replica.. The READONLY error occurs when attempting to write to a Redis replica node. In a replication group with one primary and two replicas, a failover event can promote a replica to become the new primary while the old primary becomes a replica. If the application's connection code uses the primary endpoint (which is a DNS name), DNS caching or a long TTL may cause the application to continue resolving to the old primary's IP address, now a replica. Subsequent write requests to this replica will fail with the READONLY error, causing intermittent session timeouts and data loss. Option B correctly identifies this scenario. Option A is incorrect because scaling down to a single node would not produce this specific error. Option C is incorrect because security groups would block all traffic, not just writes. Option D is incorrect because cluster mode (sharding) is unrelated to the primary-replica configuration causing the error.

Answer

The ElastiCache cluster has been scaled down to a single node, causing the primary to become unavailable.

Answer

The ElastiCache security group is blocking write traffic to the primary endpoint.

Answer

The Redis cluster mode is enabled, and the application is not using the correct cluster endpoint.

Question 11

A developer has an AWS Lambda function that processes messages from an Amazon SQS queue. The function is configured with a batch size of 10, reserved concurrency of 5, and a timeout of 5 minutes. The SQS queue has a large backlog, and CloudWatch metrics show high throttling (Throttles) for the Lambda function. The function is idempotent and can process up to 100 messages in a single invocation. What is the MOST effective way to increase throughput without increasing the reserved concurrency?

Accepted Answer

Increase the batch size to 100.. Increasing the batch size to 100 allows each Lambda invocation to process up to 100 messages from the SQS queue instead of the current 10. Since the function is idempotent and can handle 100 messages per invocation, this change maximizes the number of messages processed per invocation without altering the reserved concurrency of 5. With a batch size of 100, each of the 5 concurrent invocations can process up to 100 messages, yielding a potential throughput of 500 messages per invocation cycle, which directly reduces the backlog and throttling by consuming messages faster.

Answer

Increase the reserved concurrency to 10.

Answer

Reduce the batch size to 1.

Answer

Enable the SQS queue to use long polling.

Question 12

A developer has an AWS Lambda function that processes messages from an Amazon SQS standard queue. The function is idempotent and currently has a batch size of 10. The developer wants to increase throughput and increases the batch size to 100. After the change, CloudWatch metrics show a significant increase in throttles and the queue backlog is growing. The function's reserved concurrency is set to 10. What is the most effective action to resolve the throttling and improve throughput?

Accepted Answer

Increase the reserved concurrency of the Lambda function. Increasing the reserved concurrency from 10 to a higher value directly addresses the root cause of throttling. With a batch size of 100, each invocation processes more messages, but the function's reserved concurrency of 10 limits the maximum number of concurrent executions to 10. This means the Lambda service can only invoke the function 10 times at once, regardless of how many messages are in the queue. By raising reserved concurrency, you allow more concurrent invocations to handle the larger batches, reducing throttling and improving throughput.

Answer

Increase the memory allocation of the Lambda function

Answer

Switch the SQS queue to a FIFO queue

Answer

Decrease the batch size back to 10

Question 13

A developer is using AWS X-Ray to trace a serverless application. The application uses an AWS Lambda function to query a DynamoDB table. The trace shows that the DynamoDB subsegment takes a significant portion of the total response time. The developer wants to reduce the DynamoDB query latency. Which service should the developer integrate with the Lambda function to achieve the lowest latency for repeated read queries?

Accepted Answer

DynamoDB Accelerator (DAX). DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to 10x read performance improvement by reducing response times from milliseconds to microseconds for repeated read queries. By integrating DAX with the Lambda function, the developer can cache the results of frequent DynamoDB queries directly in memory, bypassing the read capacity units and the underlying storage engine, which directly addresses the latency bottleneck shown in the X-Ray trace.

Answer

Amazon ElastiCache for Redis

Answer

DynamoDB Global Tables

Answer

DynamoDB Streams

Question 14

A developer is building a serverless application using AWS Step Functions to orchestrate multiple AWS Lambda functions. One of the Lambda functions occasionally fails due to a transient error. The developer wants the Step Functions execution to automatically retry the failed task up to three times with exponential backoff. Which configuration should the developer set in the Step Functions state machine definition?

Accepted Answer

Use the Amazon States Language (ASL) Retry field in the Task state definition.. The Amazon States Language (ASL) provides a native Retry field within a Task state definition that allows you to specify retry policies, including a maximum retry count and exponential backoff. This is the intended mechanism for handling transient failures in Step Functions without requiring custom code or external retry logic.

Answer

Add a Retry clause in the Lambda function's configuration with a maximum retry count of 3.

Answer

Wrap the Lambda function invocation in a custom while loop within the function code.

Answer

Use the Amazon States Language Catch field in the Task state to redirect to a retry logic.

Question 15

A developer is building a REST API using Amazon API Gateway and AWS Lambda. The API receives a large number of requests with duplicate payloads from the same client within a short time window. To reduce Lambda invocations and improve performance, the developer wants to return the previously computed response for identical requests based on a unique client ID in the header. How can the developer achieve this using API Gateway features?

Accepted Answer

Enable API Gateway caching on the stage and configure the client ID header as a cache key parameter. Set a cache TTL of 5 minutes.. API Gateway caching allows you to store responses for a configurable TTL and use the client ID header as a cache key parameter. This means that when a request with the same client ID arrives within the TTL window, API Gateway returns the cached response directly without invoking the Lambda function, reducing invocations and improving performance.

Answer

Configure a usage plan with a quota and throttle settings to limit requests per client ID.

Answer

Use request validation to reject requests that have the same client ID within 5 minutes.

Answer

Reduce the Lambda function's batch size to 1 and implement caching logic inside the function using an external cache like ElastiCache.