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Amazon DVA-C02 is an exam that tests the skills and knowledge of candidates who wish to become an AWS Certified Developer - Associate. AWS Certified Developer - Associate certification is ideal for developers who have experience with AWS services and want to demonstrate their expertise in designing, developing, and deploying cloud-based applications using AWS technologies.

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To prepare for the DVA-C02 exam, candidates should have experience in developing applications using AWS services and technologies. They should be familiar with AWS SDKs, APIs, and command-line tools and have hands-on experience with AWS services such as Amazon EC2, Amazon S3, Amazon RDS, and Amazon SQS. Candidates can also benefit from taking AWS training courses, reading AWS documentation, and practicing with AWS services in a sandbox environment.

Amazon DVA-C02 (AWS Certified Developer - Associate) Exam is a certification exam designed for individuals who want to validate their skills and knowledge in developing and deploying applications on the Amazon Web Services (AWS) platform. AWS is a cloud-based platform that offers a wide range of services such as computing power, storage, and databases, among others. The Amazon DVA-C02 Exam Tests an individual's ability to design, develop, and deploy cloud-based applications using AWS services.

Amazon AWS Certified Developer - Associate Sample Questions (Q735-Q740):

NEW QUESTION # 735
A developer needs to give a new application the ability to retrieve configuration data. The application must be able to retrieve new configuration data values without the need to redeploy the application code. If the application becomes unhealthy because of a bad configuration change, the developer must be able to automatically revert the configuration change to the previous value. Which solution will meet these requirements?

A. Use AWS AppConfig to manage and store the configuration data. Integrate AWS AppConfig with Amazon CloudWatch to monitor changes to the application. Set up CloudWatch Application Signals to roll back any bad configuration changes.
B. Use AWS Secrets Manager to manage and store the configuration data. Integrate Secrets Manager with a custom AWS Config rule that has remediation actions to track changes in the application and to roll back any bad configuration changes.
C. Use AWS AppConfig to manage and store the configuration data. Integrate AWS AppConfig with Amazon CloudWatch to monitor changes to the application. Set up an alarm to automatically roll back any bad configuration changes.
D. Use AWS Secrets Manager to manage and store the configuration data. Integrate Secrets Manager with a custom AWS Config rule. Attach a custom AWS Systems Manager document to the rule that automatically rolls back any bad configuration changes.

Answer: C

Explanation:
AWS AppConfig is built for dynamic configuration management without code redeploys. You store your settings in AppConfig, and your application fetches them at runtime. AppConfig lets you attach CloudWatch alarms as deployment "rollout strategies" so that if a bad configuration causes errors (as detected by a CloudWatch metric), AppConfig automatically rolls back to the last known good version. This delivers on-the-fly updates with built-in rollback and minimal operational overhead.

NEW QUESTION # 736
A developer is building a web application that uses Amazon API Gateway to expose an AWS Lambda function to process requests from clients. During testing, the developer notices that the API Gateway times out even though the Lambda function finishes under the set time limit.
Which of the following API Gateway metrics in Amazon CloudWatch can help the developer troubleshoot the issue? (Choose two.)

A. CacheMissCount
B. Latency
C. IntegrationLatency
D. CacheHitCount
E. Count

Answer: B,C

Explanation:
The issue is caused by timeout. So the developer needs to know the latency information.
https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-metrics-and- dimensions.html
https://repost.aws/knowledge-center/api-gateway-rest-api-504-errors
https://docs.aws.amazon.com/apigateway/latest/developerguide/http-api-metrics.html

NEW QUESTION # 737
A data visualization company wants to strengthen the security of its core applications The applications are deployed on AWS across its development staging, pre-production, and production environments. The company needs to encrypt all of its stored sensitive credentials The sensitive credentials need to be automatically rotated Aversion of the sensitive credentials need to be stored for each environment Which solution will meet these requirements in the MOST operationally efficient way?

A. Configure AWS Secrets Manager versions to store different copies of the same credentials across multiple environments
B. Create a new parameter version in AWS Systems Manager Parameter Store for each environment Store the environment-specific credentials in the parameter version.
C. Configure AWS Secrets Manager to create a new secret for each environment type. Store the environment-specific credentials in the secret
D. Configure the environment variables in the application code Use different names for each environment type

Answer: C

Explanation:
Explanation
AWS Secrets Manager is the best option for managing sensitive credentials across multiple environments, as it provides automatic secret rotation, auditing, and monitoring features. It also allows storing environment-specific credentials in separate secrets, which can be accessed by the applications using the SDK or CLI. AWS Systems Manager Parameter Store does not have built-in secret rotation capability, and it requires creating individual parameters or storing the entire credential set as JSON object. Configuring the environment variables in the application code is not a secure or scalable solution, as it exposes the credentials to anyone who can access the code. References
* AWS Secrets Manager vs. Systems Manager Parameter Store
* AWS System Manager Parameter Store vs Secrets Manager vs Environment Variation in Lambda, when to use which
* AWS Secrets Manager vs. Parameter Store: Features, Cost & More

NEW QUESTION # 738
A team has an Amazon API Gateway REST API that consists of a single resource and a GET method that is backed by an AWS Lambda integration.
A developer makes a change to the Lambda function and deploys the function as a new version. The developer needs to set up a process to test the new version of the function before using the new version in production. The tests must not affect the production REST API.
Which solution will meet these requirements with the LEAST operational overhead?

A. Update the Lambda integration of the existing GET method to point to the updated version of the Lambda function. Deploy the new version.
B. Create a new stage for the REST API. Create a stage variable. Assign the stage variable to the Lambda function. Set the API Gateway integrated Lambda function name to the stage variable. Deploy the new version.
C. Create a new resource in the REST API. Add a GET method to the new resource, and add a Lambda integration to the updated version of the Lambda function. Deploy the new version.
D. Create a new REST API. Add a resource that has a single GET method that is integrated with the updated version of the Lambda function.

Answer: B

Explanation:
The requirement is to test a new Lambda version without impacting the production API, and to do so with the least operational overhead. In API Gateway (REST API), the simplest pattern is to create a separate stage (for example, test) and use a stage variable to control which Lambda function version (or alias) the integration invokes.
With option B, the developer creates a new stage and defines a stage variable (commonly something like lambdaAlias or lambdaVersion). The API method integration URI can reference this stage variable so that each stage points to a different Lambda alias/version. The production stage (for example, prod) continues to call the stable version, while the test stage calls the new version. This cleanly isolates testing traffic and prevents any effect on the production REST API because stages have distinct invoke URLs and deployments.
This approach is operationally efficient because it avoids duplicating the API definition (new REST API) and avoids creating extra resources/methods that must be maintained. It also provides a repeatable process: future versions can be tested by updating only stage variables or alias routing rather than restructuring the API.
Why not the others:
* A adds additional resources/methods and increases API surface area and maintenance.
* C duplicates the entire API, increasing overhead and configuration drift risk.
* D would impact production because it changes the integration used by the existing method.
Therefore, using a new stage + stage variables to route to the updated Lambda version meets the requirements with minimal overhead.

NEW QUESTION # 739
A company maintains an application responsible for processing several thousand external callbacks each day. The company's system administrators want to know how many callbacks are being received on a rolling basis, and they want this data available for 10 days.
The company also wants the ability to issue automated alerts if the number of callbacks exceeds the defined thresholds.
What is the MOST cost-effective way to address the need to track and alert on these statistics?

A. Push callback data to Amazon Kinesis Data Streams and invoke an AWS Lambda function that stores data in Amazon DynamoDB and sends the required alerts.
B. Push callback data to Amazon CloudWatch as a custom metric and use the CloudWatch alerting mechanisms to alert system administrators.
C. Push callback data to AWS X-Ray and use AWS Lambda to query, display, and alert on exceeded thresholds.
D. Push callback data to an Amazon RDS database that can be queried to show historical data and to alert on exceeded thresholds.

Answer: B

Explanation:
Amazon CloudWatch custom metrics are the most cost-effective fit for tracking callback counts over time and retaining the data for the required period. CloudWatch alarms can automatically notify administrators when callback volume exceeds defined thresholds without requiring a custom storage, query, or alerting system.

NEW QUESTION # 740
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