DVA-C02 Exam Dumps - AWS Certified Developer - Associate

This solution meets the requirements because it uses a PostgreSQL-compatible database that can secure and regularly rotate database credentials without requiring additional programming overhead. Amazon Aurora PostgreSQL is a relational database service that is compatible with PostgreSQL and offers high performance, availability, and scalability. AWS Secrets Manager is a service that helps you protect secrets needed to access your applications, services, and IT resources. You can store database credentials in AWS Secrets Manager and use them to access your Aurora PostgreSQL database. You can also enable automatic rotation of your secrets according to a schedule or an event. AWS Secrets Manager handles the complexity of rotating secrets for you, such as generating new passwords and updating your database with the new credentials. Using Amazon DynamoDB for the database will not meet the requirements because it is a NoSQL database that is not compatible with PostgreSQL. Using AWS Systems Manager Parameter Store for storing and rotating database credentials will require additional programming overhead to integrate with your database.

The Deployment Preference Type property specifies how traffic should be shifted between versions of a Lambda function1. The Canary10Percent10Minutes option means that 10% of the traffic is immediately shifted to the new version, and after 10 minutes, the remaining 90% of the traffic is shifted1. This matches the requirement of shifting 10% of the traffic for the first 10 minutes, and then switching all traffic to the new version.

The AutoPublishAlias property enables AWS SAM to automatically create and update a Lambda alias that points to the latest version of the function1. This is required to use the Deployment Preference Type property1. The alias name can be specified by the developer, and it can be used to invoke the function with the latest code.

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

This solution will meet the requirements by creating a single S3 bucket policy that denies access to the S3 bucket unless the request comes from one of the specified VPC endpoints. The aws:SourceVpce condition key is used to match the ID of the VPC endpoint that is used to access the S3 bucket. The StringNotEquals condition operator is used to negate the condition, so that only requests from the listed VPC endpoints are allowed. Option A is not optimal because it will create multiple S3 bucket policies, which is not possible as only one bucket policy can be attached to an S3 bucket. Option B is not optimal because it will use the aws:SourceVpc condition key, which matches the ID of the VPC that is used to access the S3 bucket, not the VPC endpoint. Option C is not optimal because it will use the StringNotEquals condition operator with a single value, which will deny access to the S3 bucket from all VPC endpoints except one.

Why Option B is Correct:

Amazon S3 Standard provides immediate access to files and is cost-effective for files that need to be accessed within 5 minutes.

By adding the S3 location to the SQS queue, you avoid transferring large files directly, which is both more efficient and scalable.

Why Other Options are Incorrect:

Option A: S3 Glacier Deep Archive is designed for archival storage with retrieval times ranging from minutes to hours, which does not meet the 5-minute requirement.

Option C: Amazon EBS is designed for block storage attached to EC2 instances, which adds unnecessary complexity and cost.

Option D: SQS is not designed to handle large file content directly and has message size limits (256 KB).

AWS Documentation References:

Amazon S3 Overview

Amazon SQS Best Practices

Problem: CloudFormation can't find the custom AMI in the target region (us-west-1) because AMIs are region-specific.

Copying AMIs:

AMIs can be copied across regions, maintaining their configuration.

This approach minimizes operational overhead as the existing CloudFormation template can be reused with a minor update.

Updating the Template:

Modify the CloudFormation template in us-west-1 to reference the newly copied AMI's ID in that region.

To meet the requirement:

Users must be able to upload (PutObject) and read (GetObject) files but not delete them.

Option D ensures users cannot delete files by omitting the s3:DeleteObject action while allowing s3:GetObject and s3:PutObject.

Option A: Includes s3:DeleteObject, which allows users to delete files and does not meet the requirement.

Option B: Contains unrelated actions like CreateBucket, which is not relevant here.

Option C: Adds s3:PutObjectRetention, which is unnecessary and does not restrict DeleteObject.