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Amazon DVA-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Development with AWS Services	32%	- Use data stores in application development 1. Amazon DynamoDB 2. Amazon EFS 3. Amazon ElastiCache 4. Amazon RDS 5. Amazon S3 - Develop code for AWS serverless applications 1. AWS Step Functions 2. AWS AppSync 3. Amazon EventBridge 4. Amazon API Gateway - Develop code for applications hosted on AWS 1. AWS CloudFormation 2. Amazon DynamoDB 3. AWS CLI 4. AWS Lambda 5. Amazon SQS 6. AWS SDKs 7. Amazon API Gateway 8. Amazon SNS 9. Amazon S3 10. Amazon Cognito
Topic 2: Deployment	24%	- Prepare application artifacts to be deployed to AWS 1. AWS CloudFormation 2. Lambda deployment packages 3. AWS SAM 4. Docker containers 5. AWS CDK - Automate deployment testing 1. AWS CodeCommit 2. AWS CodeDeploy 3. AWS CodePipeline 4. AWS CodeBuild - Deploy code by using AWS CI/CD services 1. AWS CodeCommit 2. GitHub integration 3. AWS CodeDeploy 4. Blue/green deployments 5. Canary deployments 6. AWS CodePipeline 7. Rolling deployments 8. AWS CodeBuild - Test applications in development environments 1. AWS SAM local 2. Mocking AWS services 3. Local testing of Lambda functions
Topic 3: Troubleshooting and Optimization	18%	- Optimize applications by using AWS services and features 1. Data storage optimization 2. Caching strategies (ElastiCache, CloudFront) 3. Cost optimization 4. Memory and compute optimization 5. Concurrency and throttling - Assist in a root cause analysis 1. Amazon CloudWatch Logs 2. Application logging 3. AWS CloudTrail 4. AWS X-Ray 5. Error handling and retries - Instrument code for observability 1. Amazon CloudWatch metrics 2. Structured logging 3. Amazon CloudWatch alarms 4. AWS X-Ray 5. Custom metrics
Topic 4: Security	26%	- Implement authentication and/or authorization for applications and AWS services 1. Identity federation (SAML, OpenID Connect, Amazon Cognito) 2. Role-based access control (RBAC) 3. User pools and identity pools in Amazon Cognito 4. Resource-based policies, service policies, and principal policies 5. Bearer tokens (JWT, OAuth, AWS STS) - Implement encryption by using AWS services 1. AWS Secrets Manager 2. Amazon S3 encryption 3. AWS CloudHSM 4. AWS KMS 5. AWS Certificate Manager - Manage sensitive data in application code 1. AWS Secrets Manager 2. Encryption of data at rest and in transit 3. AWS Systems Manager Parameter Store 4. Environment variables

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2026 Amazon DVA-C02: Efficient Test AWS Certified Developer - Associate Sample Questions

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Amazon AWS Certified Developer - Associate Sample Questions (Q460-Q465):

NEW QUESTION # 460
A developer deploys an AWS Lambda function that writes data to Amazon DynamoDB. Amazon CloudWatch metrics show that some Lambda invocations result in errors. However, no CloudWatch log entries exist for the function. The function source code includes logging statements before the write operation.
What should the developer do to resolve the missing CloudWatch Logs entries?

A. Enable AWS X-Ray active tracing for the function.
B. Enable CloudWatch Lambda Insights and redeploy the function.
C. Add CloudWatch as a trusted identity in the Lambda execution role.
D. Grant the Lambda execution role permission to write logs to Amazon CloudWatch Logs.

Answer: D

Explanation:
AWS Lambda automatically integrates with Amazon CloudWatch Logs, but logging only works if the function's execution role has the correct IAM permissions. AWS documentation states that Lambda functions require permissions for logs:CreateLogGroup, logs:CreateLogStream, and logs:PutLogEvents to successfully publish log entries.
In this scenario, CloudWatch metrics show errors, which confirms that the function is executing. The absence of logs despite logging statements in the code strongly indicates a missing IAM permission issue, not a runtime or tracing issue.
CloudWatch Lambda Insights (Option B) provides performance metrics but does not enable basic logging.
AWS X-Ray (Option C) provides distributed tracing but does not replace CloudWatch Logs. Option D is invalid because CloudWatch does not assume roles to collect logs.
AWS documentation explicitly identifies missing log permissions as the most common cause of absent Lambda logs. Therefore, granting the Lambda execution role the appropriate CloudWatch Logs permissions resolves the issue.

NEW QUESTION # 461
A developer is building a serverless application by using the AWS Serverless Application Model (AWS SAM). The developer is currently testing the application in a development environment.
When the application is nearly finished, the developer will need to set up additional testing and staging environments for a quality assurance team.
The developer wants to use a feature of the AWS SAM to set up deployments to multiple environments.
Which solution will meet these requirements with the LEAST development effort?

A. Add a configuration file in TOML format to group configuration entries to every environment. Add a table for each testing and staging environment. Deploy updates to the environments by using the sam deploy command and the --config-env flag that corresponds to each environment.
B. Create one AWS SAM configuration file that has default parameters. Perform updates to the testing and staging environments by using the --parameter-overrides flag in the AWS SAM CLI and the parameters that the updates will override.
C. Create additional AWS SAM templates for each testing and staging environment. Write a custom shell script that uses the sam deploy command and the --template-file flag to deploy updates to the environments.
D. Use the existing AWS SAM template. Add additional parameters to configure specific attributes for the serverless function and database table resources that are in each environment. Deploy updates to the testing and staging environments by using the sam deploy command.

Answer: A

NEW QUESTION # 462
A developer maintains a serverless application that uses AWS Lambda to process financial transaction files that have been uploaded to an Amazon S3 bucket. The developer has scheduled a Lambda function to run once each hour to process the files.
Over time, the frequency of file uploads to the S3 bucket increases. The increasing number of file uploads occasionally causes the Lambda function to exceed its 15-minute execution timeout.
The developer needs to modify the application's architecture to handle increased file uploads and decrease file processing time. The developer must also ensure that the application does not duplicate the processing of files.
Which solution will meet these requirements?

A. Use Amazon EventBridge Scheduler to run the Lambda function every 5 minutes to scan the S3 bucket.
B. Configure Amazon S3 events to invoke the Lambda function when each file is uploaded. Log processed transaction IDs to Amazon DynamoDB.
C. Move the application logic to a single Amazon EC2 instance to handle processing more effectively.
D. Poll the Amazon S3 bucket continuously by using the Lambda function and each object's LastModified timestamp.

Answer: B

Explanation:
The hourly scheduled batch model is causing timeouts because the function must process an ever-growing backlog in a single invocation, which can exceed AWS Lambda's maximum 15-minute runtime. A best- practice serverless refactor is to switch from "periodic polling" to an event-driven approach where work is split into smaller, independent units that can scale horizontally.
Configuring Amazon S3 Event Notifications to invoke Lambda on each object upload turns each file into its own discrete event. Instead of one long-running invocation that scans and processes many files, Lambda executes quickly per upload and automatically scales with incoming volume (subject to concurrency and downstream limits). This directly reduces processing time per run and eliminates the backlog buildup that leads to timeouts.
The remaining requirement is preventing duplicate processing. S3 event delivery is designed to be highly reliable, and applications should be built with idempotency in mind. Recording processed identifiers (such as a transaction ID, object key + version ID, or an application-level unique ID) in Amazon DynamoDB allows the function to check whether an item has already been processed before performing writes or downstream actions. Using DynamoDB with a conditional write (for example, "insert only if not exists") is a common pattern to ensure exactly-once effect even when events are delivered more than once.
The other options do not meet the requirements as well: running more frequently (C) or continuously polling (D) still wastes time scanning and can still create duplicate work; moving to a single EC2 instance (B) reduces elasticity and increases operational overhead, and it doesn't inherently solve deduplication or scaling.
Therefore, A provides scalable, event-driven processing and a straightforward deduplication mechanism.

NEW QUESTION # 463
A company runs an application on AWS The application stores data in an Amazon DynamoDB table Some queries are taking a long time to run These slow queries involve an attribute that is not the table's partition key or sort key The amount of data that the application stores in the DynamoDB table is expected to increase significantly. A developer must increase the performance of the queries.
Which solution will meet these requirements'?

A. Turn on read capacity auto scaling for the DynamoDB table. Increase the maximum read capacity units (RCUs).
B. Perform a parallel scan operation by issuing individual scan requests in the parameters specify the segment for the scan requests and the total number of segments for the parallel scan.
C. Increase the page size for each request by setting the Limit parameter to be higher than the default value Configure the application to retry any request that exceeds the provisioned throughput.
D. Create a global secondary index (GSI). Set query attribute to be the partition key of the index

Answer: D

Explanation:
* Global Secondary Index (GSI): GSIs enable alternative query patterns on a DynamoDB table by using different partition and sort keys.
* Addressing Query Bottleneck: By making the slow-query attribute the GSI's partition key, you optimize queries on that attribute.
* Scalability: GSIs automatically scale to handle increasing data volumes.
References:
Amazon DynamoDB Global Secondary Indexes: https://docs.aws.amazon.com/amazondynamodb/latest
/developerguide/GSI.html

NEW QUESTION # 464
A company recently deployed an AWS Lambda function. A developer notices an increase in the function throttle metrics in Amazon CloudWatch.
What are the MOST operationally efficient solutions to reduce the function throttling? (Choose two.)

A. Increase the maximum age of events in Lambda.
B. Increase the function's reserved concurrency.
C. Request a service quota change for increased concurrency.
D. Migrate the function to Amazon Elastic Kubernetes Service (Amazon EKS).
E. Add the lambda:GetFunctionConcurrency action to the execution role.

Answer: B,C

NEW QUESTION # 465
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