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Amazon DVA-C02 Certification Exam is also suitable for professionals who want to pursue a career in cloud computing and want to establish themselves as experts in AWS development. Earning this certification demonstrates a candidate's comprehensive knowledge of AWS services and their ability to design, develop, and deploy secure, scalable, and fault-tolerant applications in the cloud. Overall, the Amazon DVA-C02 certification is a valuable credential for developers seeking to advance their career in cloud computing.
Amazon DVA-C02 exam covers a range of topics related to AWS development, including AWS core services, security and compliance, development and deployment, and troubleshooting. Candidates must have a good understanding of AWS services such as EC2, S3, DynamoDB, and Elastic Beanstalk, as well as knowledge of programming languages such as Java, Python, and Node.js. Passing this certification exam demonstrates that the individual has the necessary skills to design and develop scalable, secure, and fault-tolerant applications on the AWS cloud platform.
NEW QUESTION # 186
A developer needs to export the contents of several Amazon DynamoDB tables into Amazon S3 buckets to comply with company data regulations. The developer uses the AWS CLI to run commands to export from each table to the proper S3 bucket. The developer sets up AWS credentials correctly and grants resources appropriate permissions. However, the exports of some tables fail. What should the developer do to resolve this issue?
Answer: A
Explanation:
When exporting data from DynamoDB to S3 using the AWS CLI or other tools, it is important to ensure that the DynamoDB table and the target S3 bucket are in the same AWS Region. If the S3 bucket is in a different region from the DynamoDB table, the export will fail because data cannot be transferred across regions without additional configuration.
NEW QUESTION # 187
An application development team decides to use AWS X-Ray to monitor application code to analyze performance and perform root cause analysis.
What does the team need to do to begin using X-Ray? (Select TWO.)
Answer: B,E
Explanation:
To begin using AWS X-Ray, the team must (1) instrument the application to emit trace segments
/subsegments and (2) ensure there is a component that can send trace data to the X-Ray service.
Instrumenting code is typically done with the AWS X-Ray SDK (language-specific). This adds tracing to inbound requests and downstream calls, creates segments/subsegments, and attaches annotations/metadata.
That corresponds to Option C.
For many compute environments (especially EC2, ECS on EC2, and on-prem servers), the application sends trace data via the X-Ray daemon/agent running locally. The daemon batches segments and forwards them to the X-Ray service endpoint. That corresponds to Option D.
Option B is helpful after traces exist (using the X-Ray console or CloudWatch ServiceLens), but it is not a prerequisite to "begin using" X-Ray.
Option A is unrelated. X-Ray does not require SQS for instrumentation output.
Option E is incorrect because X-Ray stores trace data in its managed backend; you do not create a DynamoDB table for trace logs.
Therefore, the team needs to instrument the code with the X-Ray SDK and install/run the X-Ray agent
/daemon on the servers where the application runs.
NEW QUESTION # 188
A developer is creating a template that uses AWS CloudFormation to deploy an application. The application is serverless and uses Amazon API Gateway, Amazon DynamoDB, and AWS Lambda.
Which AWS service or tool should the developer use to define serverless resources in YAML?
Answer: D
Explanation:
AWS Serverless Application Model (AWS SAM) is an open-source framework that enables developers to build and deploy serverless applications on AWS. AWS SAM uses a template specification that extends AWS CloudFormation to simplify the definition of serverless resources such as API Gateway, DynamoDB, and Lambda. The developer can use AWS SAM to define serverless resources in YAML and deploy them using the AWS SAM CLI.
NEW QUESTION # 189
A developer is building an application that needs to store an API key. An AWS Lambda function needs to use the API key. The developer's company requires secrets to be encrypted at rest by an AWS KMS key. The company must control key rotation.
Which solutions will meet these requirements? (Select TWO.)
Answer: C,E
Explanation:
The requirements are: (1) store an API key that a Lambda function will use, (2) ensure the secret is encrypted at rest using AWS KMS, and (3) the company must control key rotation, which implies using a customer managed KMS key (CMK) rather than an AWS managed key.
Option C meets the requirements by storing the API key in AWS Systems Manager Parameter Store as a SecureString parameter encrypted with a customer managed KMS key. SecureString is designed for sensitive configuration data and integrates with KMS so the organization can choose the CMK, manage its lifecycle, and control rotation policies. The Lambda function can retrieve the parameter at runtime using the AWS SDK, and IAM policies can tightly control access to the parameter and to the KMS key.
Option E also meets the requirements by storing the API key in a Lambda environment variable encrypted with a customer managed KMS key. Lambda encrypts environment variables at rest and allows you to specify a customer managed KMS key for encryption. This gives the company control over key rotation and key policy, satisfying the "must control key rotation" requirement. The function reads the value from the environment at runtime without additional network calls.
Why the other options fail:
* A uses an AWS managed KMS key, which does not satisfy the requirement for the company to control rotation (you cannot manage rotation of AWS managed keys in the same way).
* B is a plain String parameter, which is not encrypted as a secret and does not meet the at-rest encryption requirement.
* D uses an AWS managed KMS key, again failing the company-controlled rotation requirement.
Therefore, the two valid solutions are C (Parameter Store SecureString with a customer managed CMK) and E (Lambda environment variable encrypted with a customer managed CMK).
NEW QUESTION # 190
A developer is designing a serverless application with two AWS Lambda functions to process photos. One Lambda function stores objects in an Amazon S3 bucket and stores the associated metadata in an Amazon DynamoDB table. The other Lambda function fetches the objects from the S3 bucket by using the metadata from the DynamoDB table. Both Lambda functions use the same Python library to perform complex computations and are approaching the quota for the maximum size of zipped deployment packages.
What should the developer do to reduce the size of the Lambda deployment packages with the LEAST operational overhead?
Answer: A
Explanation:
AWS Lambda is a service that lets developers run code without provisioning or managing servers. Lambda layers are a distribution mechanism for libraries, custom runtimes, and other dependencies. The developer can create a Lambda layer with the required Python library and use the layer in both Lambda functions. This will reduce the size of the Lambda deployment packages and avoid reaching the quota for the maximum size of zipped deployment packages. The developer can also benefit from using layers to manage dependencies separately from function code.
NEW QUESTION # 191
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