DVA-C02 Exam Dumps - AWS Certified Developer - Associate

CodeDeploy Predefined Configurations: CodeDeploy offers built-in deployment configurations for common scenarios.

Canary Deployment: Canary deployments gradually shift traffic to a new version, ideal for controlled rollouts like this requirement.

CodeDeployDefault.ECSCanary10Percent15Minutes: This configuration matches the company's requirements, shifting 10% of traffic initially and then completing the rollout after 15 minutes.

DynamoDB access control operates at the table and item level, not at individual attribute deny rules in IAM or resource-based policies. Therefore, Option A is not supported. Encrypting the entire table with KMS (Option B) protects data at rest but does not prevent the Lambda function from reading sensitive attributes once access is granted.

To ensure the Lambda function cannot access PII, AWS documentation recommends controlling which attributes are returned from queries and scans. Using a projection expression (Option E) ensures that only non-PII attributes are returned to the function, even if PII exists in the item. This is a standard DynamoDB best practice for limiting data exposure.

For additional protection, client-side or application-level encryption should be used for sensitive fields. Option C uses envelope encryption with AWS KMS to encrypt only PII attributes. Even if the function retrieves the encrypted attribute, it cannot decrypt the data without KMS permissions. This ensures strong isolation of sensitive data.

ABAC (Option D) controls access to resources, not attributes, and therefore does not satisfy the requirement.

Thus, combining attribute-level encryption with projection expressions meets the security requirement.

The solution that will meet the requirements with the most operational efficiency is to create a new CodePipeline stage that occurs after the container image is built. Configure ECR basic image scanning to scan on image push. Use an AWS Lambda function as the action provider. Configure the Lambda function to check the scan results and to fail the pipeline if there are findings. This way, the container image is analyzed earlier in the CI/CD pipeline and any vulnerabilities are detected and reported before deploying to the EKS cluster. The other options either delay the analysis until after deployment, which increases the risk of exposing insecure images, or perform analysis on the source code instead of the container image, which may not capture all the dependencies and configurations that affect the security posture of the image.

This solution will meet the requirements by using Amazon CloudWatch Logs to capture and analyze the logs from API Gateway. Amazon CloudWatch Logs is a service that monitors, stores, and accesses log files from AWS resources. The developer can turn on execution logging and access logging in Amazon CloudWatch Logs for the API stage, which enables logging information about API execution and client access to the API. The developer can create a CloudWatch Logs log group, which is a collection of log streams that share the same retention, monitoring, and access control settings. The developer can specify the Amazon Resource Name (ARN) of the log group for the API stage, which instructs API Gateway to send the logs to the specified log group. The developer can then examine the logs to determine the cause of the HTTP 400 response errors. Option A is not optimal because it will create an Amazon Kinesis Data Firehose delivery stream to receive API call logs from API Gateway, which may introduce additional costs and complexity for delivering and processing streaming data. Option B is not optimal because it will turn on AWS CloudTrail Insights and create a trail, which is a feature that helps identify and troubleshoot unusual API activity or operational issues, not HTTP response errors. Option C is not optimal because it will turn on AWS X-Ray for the API stage, which is a service that helps analyze and debug distributed applications, not HTTP response errors.

An SQS FIFO queue is a type of queue that preserves the order of messages and ensures that each message is delivered and processed only once1. This is suitable for the scenario where the developer wants to ensure that there are no out-of-order updates in the legacy system.

The visibility timeout value is the amount of time that a message is invisible in the queue after a consumer receives it2. This prevents other consumers from processing the same message simultaneously. If the consumer does not delete the message before the visibility timeout expires, the message becomes visible again and another consumer can receive it2.

In this scenario, the developer needs to configure the visibility timeout value to be longer than the maximum processing time of the legacy system, which is 5 minutes. This will ensure that the message remains invisible in the queue until the legacy system finishes processing it and deletes it. This will prevent duplicate or out-of-order processing of messages by the legacy system.

Comprehensive and Detailed Explanation (250–300 words):

Amazon SQS uses a visibility timeout to prevent other consumers from processing a message while it is being handled. If a Lambda function does not complete processing before the visibility timeout expires, the message becomes visible again and can be processed a second time.

AWS documentation states that the SQS visibility timeout should be greater than the maximum Lambda execution time. If not, messages may be delivered multiple times, even though processing is still in progress.

Increasing the Lambda timeout or memory does not directly affect message visibility. Increasing batch size can worsen the problem by increasing processing time.

Therefore, increasing the SQS visibility timeout is the correct and AWS-recommended solution.

AWS Lambda combined with Amazon S3 event notifications provides the most cost-effective and lowest-latency solution for processing objects uploaded to S3. AWS documentation states that S3 event notifications can invoke Lambda functions immediately after object creation, eliminating delays associated with polling or scheduled execution.

Because the data arrives only about 10 times per day and processing completes quickly, Lambda is ideal due to its pay-per-invocation pricing model. There is no need to maintain always-on infrastructure, which significantly reduces cost compared to running EC2 instances. The developer pays only for execution time and requests.

Using CloudWatch alarms (Option A) or scheduled events (Option C) introduces unnecessary latency and complexity. These approaches either delay processing or require periodic checks rather than reacting instantly to object uploads. Polling from EC2 (Option D) is the most expensive and least efficient option because it requires continuously running compute resources.

AWS best practices for event-driven architectures explicitly recommend S3 event notifications → Lambda for near-real-time processing of uploaded data. This approach minimizes operational overhead, achieves near-zero idle cost, and provides the fastest possible response to new data.

Therefore, invoking a Lambda function directly through an S3 event notification is the optimal solution.