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Amazon AWS Certified Developer - Associate Sample Questions (Q599-Q604):

NEW QUESTION # 599
A developer is migrating a containerized application from an on-premises environment to the AWS Cloud. The developer is using the AWS CDK to provision a container in Amazon ECS on AWS Fargate. The container is behind an Application Load Balancer (ALB).
When the developer deploys the stack, the deployment fails because the ALB fails health checks. The developer needs to resolve the failed health checks.
Which solutions will meet this requirement? (Select TWO.)

A. Confirm that the target group port matches the port mappings in the ECS task definition.
B. Confirm that the ALB listener on the mapped port has a default action that forwards to the correct target group.
C. Confirm that the capacity providers for the container have been provisioned and are properly sized.
D. Confirm that a hosted zone associated with the ALB matches a hosted zone that is referenced in the ECS task definition.
E. Confirm that the ALB listener on the mapped port has a default action that redirects to the application's health check path endpoint.

Answer: A,B

Explanation:
Option B: The target group port in the ALB must match the port specified in the ECS task definition. If there is a mismatch, the ALB health check will fail since it cannot correctly route traffic to the container.
Option E: The ALB listener must have a default action that forwards requests to the correct target group associated with the ECS service. If this configuration is missing, the health check will fail as no traffic is routed to the service.
Option A is irrelevant to resolving health check issues since capacity providers relate to provisioning compute capacity.
Option C (hosted zone) is not directly related to ALB health checks.
Option D (redirecting traffic) is not related to ECS health check configurations.
Reference:
AWS ECS Health Check Documentation
Reference:
AWS ECS Health Check Documentation

NEW QUESTION # 600
A developer wants to deploy a new version of an AWS Elastic Beanstalk application. During deployment the application must maintain full capacity and avoid service interruption. Additionally, the developer must minimize the cost of additional resources that support the deployment.
Which deployment method should the developer use to meet these requirements?

A. Bluegreen
B. Immutable
C. Rolling with additional batch
D. All at once

Answer: C

Explanation:
This solution will meet the requirements by using a rolling with additional batch deployment method, which deploys the new version of the application to a separate group of instances and then shifts traffic to those instances in batches. This way, the application maintains full capacity and avoids service interruption during deployment, as well as minimizes the cost of additional resources that support the deployment. Option A is not optimal because it will use an all at once deployment method, which deploys the new version of the application to all instances simultaneously, which may cause service interruption or downtime during deployment. Option C is not optimal because it will use a blue/green deployment method, which deploys the new version of the application to a separate environment and then swaps URLs with the original environment, which may incur more costs for additional resources that support the deployment. Option D is not optimal because it will use an immutable deployment method, which deploys the new version of the application to a fresh group of instances and then redirects traffic to those instances, which may also incur more costs for additional resources that support the deployment.

NEW QUESTION # 601
A developer is modifying an existing AWS Lambda function. While checking the code, the developer notices hardcoded parameter values for an Amazon RDS for SQL Server user name, password, database, host, and port. There are also hardcoded parameter values for an Amazon DynamoDB table, an Amazon S3 bucket, and an Amazon Simple Notification Service (Amazon SNS) topic.
The developer wants to securely store the parameter values outside the code in an encrypted format and wants to turn on rotation for the credentials. The developer also wants to be able to reuse the parameter values from other applications and to update the parameter values without modifying code.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an RDS database secret in AWS Secrets Manager. Set the user name, password, database, host, and port. Turn on secret rotation. Create SecureString parameters in AWS Systems Manager Parameter Store for the DynamoDB table, S3 bucket, and SNS topic.
B. Create RDS database parameters in AWS Systems Manager Parameter Store for the user name, password, database, host, and port. Create encrypted Lambda environment variables for the DynamoDB table, S3 bucket, and SNS topic. Create a Lambda function and set the logic for the credentials rotation task. Schedule the credentials rotation task in Amazon EventBridge.
C. Create an RDS database secret in AWS Secrets Manager. Set the user name, password, database, host, and port. Turn on secret rotation. Create encrypted Lambda environment variables for the DynamoDB table, S3 bucket, and SNS topic.
D. Create RDS database parameters in AWS Systems Manager Parameter Store for the user name, password, database, host, and port. Store the DynamoDB table, S3 bucket, and SNS topic in Amazon S3. Create a Lambda function and set the logic for the credentials rotation. Invoke the Lambda function on a schedule.

Answer: A

Explanation:
https://docs.aws.amazon.com/systems-manager/latest/userguide/integration-ps- secretsmanager.html

NEW QUESTION # 602
A company is building an application for stock trading. The application needs sub-millisecond latency for processing trade requests. The company uses Amazon DynamoDB to store all the trading data that is used to process each trading request A development team performs load testing on the application and finds that the data retrieval time is higher than expected. The development team needs a solution that reduces the data retrieval time with the least possible effort.
Which solution meets these requirements'?

A. Add retries with exponential back off for DynamoDB queries.
B. Add local secondary indexes (LSis) for the trading data.
C. Use DynamoDB Accelerator (DAX) to cache the trading data.
D. Store the trading data m Amazon S3 and use S3 Transfer Acceleration.

Answer: C

Explanation:
This solution will meet the requirements by using DynamoDB Accelerator (DAX), which is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10 times performance improvement - from milliseconds to microseconds - even at millions of requests per second. The developer can use DAX to cache the trading data that is used to process each trading request, which will reduce the data retrieval time with the least possible effort. Option A is not optimal because it will add local secondary indexes (LSIs) for the trading data, which may not improve the performance or reduce the latency of data retrieval, as LSIs are stored on the same partition as the base table and share the same provisioned throughput. Option B is not optimal because it will store the trading data in Amazon S3 and use S3 Transfer Acceleration, which is a feature that enables fast, easy, and secure transfers of files over long distances between S3 buckets and clients, not between DynamoDB and clients. Option C is not optimal because it will add retries with exponential backoff for DynamoDB queries, which is a strategy to handle transient errors by retrying failed requests with increasing delays, not by reducing data retrieval time.
References: [DynamoDB Accelerator (DAX)], [Local Secondary Indexes]

NEW QUESTION # 603
A company created an application to consume and process data. The application uses Amazon SQS and AWS Lambda functions. The application is currently working as expected, but it occasionally receives several messages that it cannot process properly. The company needs to clear these messages to prevent the queue from becoming blocked. A developer must implement a solution that makes queue processing always operational. The solution must give the company the ability to defer the messages with errors and save these messages for further analysis. What is the MOST operationally efficient solution that meets these requirements?

A. Create a new SQS queue. Set the new queue as a dead-letter queue for the application queue.
Configure the Maximum Receives setting.
B. Change the SQS queue to a FIFO queue. Configure the message retention period to 0 seconds.
C. Configure an Amazon CloudWatch alarm for Lambda function errors. Publish messages to an Amazon SNS topic to notify administrator users.
D. Configure Amazon CloudWatch Logs to save the error messages to a separate log stream.

Answer: A

Explanation:
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.
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.

NEW QUESTION # 604
......

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