Amazon Web Services Updated DVA-C02 Exam Questions and Answers by renee

For in-place deployments, AWS CodeDeploy uses a set of predefined hooks that run in a specific order during each deployment lifecycle event. The hooks are ApplicationStop, BeforeInstall, AfterInstall, ApplicationStart, and ValidateService. The run order of the hooks for in-place deployments is as follows:

ApplicationStop: This hook runs first on all instances and stops the current application that is running on the instances.

BeforeInstall: This hook runs after ApplicationStop on all instances and performs any tasks required before installing the new application revision.

AfterInstall: This hook runs after BeforeInstall on all instances and performs any tasks required after installing the new application revision.

ApplicationStart: This hook runs after AfterInstall on all instances and starts the new application that has been installed on the instances.

ValidateService: This hook runs last on all instances and verifies that the new application is running properly on the instances.

Using a dead-letter queue (DLQ) with Amazon SQS is the most operationally efficient solution for handling unprocessable messages.

Amazon SQS Dead-Letter Queue:

A DLQ is used to capture messages that fail processing after a specified number of attempts.

Allows the application to continue processing other messages without being blocked.

Messages in the DLQ can be analyzed later for debugging and resolution.

Why DLQ is the Best Option:

Operational Efficiency: Automatically defers messages with errors, ensuring the queue is not blocked.

Analysis Ready: Messages in the DLQ can be inspected to identify recurring issues.

Scalable: Works seamlessly with Lambda and SQS at scale.

Why Not Other Options:

Option A: Logs the messages but does not resolve the queue blockage issue.

Option C: FIFO queues and 0-second retention do not provide error handling or analysis capabilities.

Option D: Alerts administrators but does not handle or store the unprocessable messages.

Steps to Implement:

Create a new SQS queue to serve as the DLQ.

Attach the DLQ to the primary queue and configure the Maximum Receives setting.

Using Amazon SQS Dead-Letter Queues

Best Practices for Using Amazon SQS with AWS Lambda

Comprehensive and Detailed Explanation (250–300 words):

AWS best practices for multi-tenant isolation recommend session-based access control using IAM session tags. Session tags allow temporary credentials to be scoped dynamically based on tenant context.

First, the data access role must allow sts:TagSession (Option A) so tenant identifiers can be passed securely at runtime. The Lambda function assumes this role and includes the tenant name as a session tag (Option F).

Next, IAM policies on the data access role restrict access to S3 object prefixes and DynamoDB partition keys by evaluating the session tag (Option C). This ensures that even if a bug occurs, the role cannot access data belonging to another tenant.

Resource-based DynamoDB policies and RCPs are unnecessary here and add complexity.

This approach follows AWS’s recommended attribute-based access control (ABAC) model and provides strong tenant isolation with minimal operational overhead.

Problem: Large bundle size increases Lambda deployment time.

Lambda Layers: Layers let you package dependencies separately from your function code. This optimizes the deployment package, making updates faster.

Modularization: Breaking down dependencies into layers improves code organization and reusability.