Amazon DVA-C02 Practice Exams
Last updated on Oct 23,2026- Exam Code: DVA-C02
- Exam Name: AWS Certified Developer - Associate
- Certification Provider: Amazon
- Latest update: Oct 23,2026
A developer is building an application that includes an AWS Lambda function that is written in .NET Core. The Lambda function’s code needs to interact with Amazon DynamoDB tables and Amazon S3 buckets. The developer must minimize the Lambda function’s deployment time and invocation duration.
Which solution will meet these requirements?
- A . Increase the Lambda function’s memory.
- B . Include the entire AWS SDK for .NET in the Lambda function’s deployment package.
- C . Include only the AWS SDK for .NET modules for DynamoDB and Amazon S3 in the Lambda function’s deployment package.
- D . Configure the Lambda function to download the AWS SDK for .NET from an S3 bucket at runtime.
A company has an application that runs as a series of AWS Lambda functions. Each Lambda function receives data from an Amazon Simple Notification Service (Amazon SNS) topic and writes the data to an Amazon Aurora DB instance.
To comply with an information security policy, the company must ensure that the Lambda functions all use a single securely encrypted database connection string to access Aurora.
Which solution will meet these requirements’?
- A . Use IAM database authentication for Aurora to enable secure database connections for ail the Lambda functions.
- B . Store the credentials and read the credentials from an encrypted Amazon RDS DB instance.
- C . Store the credentials in AWS Systems Manager Parameter Store as a secure string parameter.
- D . Use Lambda environment variables with a shared AWS Key Management Service (AWS KMS) key for encryption.
A
Explanation:
This solution will meet the requirements by using IAM database authentication for Aurora, which enables using IAM roles or users to authenticate with Aurora databases instead of using passwords or other secrets. The developer can use IAM database authentication for Aurora to enable secure database connections for all the Lambda functions that access Aurora DB instance. The developer can create an IAM role with permission to connect to Aurora DB instance and attach it to each Lambda function. The developer can also configure Aurora DB instance to use IAM database authentication and enable encryption in transit using SSL certificates. This way, the Lambda functions can use a single securely encrypted database connection string to access Aurora without needing any secrets or passwords.
Option B is not optimal because it will store the credentials and read them from an encrypted Amazon RDS DB instance, which may introduce additional costs and complexity for managing and accessing another RDS DB instance.
Option C is not optimal because it will store the credentials in AWS Systems Manager Parameter Store as a secure string parameter, which may
require additional steps or permissions to retrieve and decrypt the credentials from Parameter Store.
Option D is not optimal because it will use Lambda environment variables with a shared AWS Key Management Service (AWS KMS) key for encryption, which may not be secure or scalable as environment variables are stored as plain text unless encrypted with AWS KMS.
Reference: [IAM Database Authentication for MySQL and PostgreSQL], [Using SSL/TLS to Encrypt a Connection to a DB Instance]
A company’s website runs on an Amazon EC2 instance and uses Auto Scaling to scale the environment during peak times. Website users across the world ate experiencing high latency flue lo sialic content on theEC2 instance. even during non-peak hours.
When companion of steps mill resolves the latency issue? (Select TWO)
- A . Double the Auto Scaling group’s maximum number of servers
- B . Host the application code on AWS lambda
- C . Scale vertically by resizing the EC2 instances
- D . Create an Amazon Cloudfront distribution to cache the static content
- E . Store the application’s sialic content in Amazon S3
DE
Explanation:
The combination of steps that will resolve the latency issue is to create an Amazon CloudFront distribution to cache the static content and store the application’s static content in Amazon S3. This way, the company can use CloudFront to deliver the static content from edge locations that are closer to the website users, reducing latency and improving performance. The company can also use S3 to store the static content reliably and cost-effectively, and integrate it with CloudFront easily. The other options either do not address the latency issue, or are not necessary or feasible for the given scenario.
Reference: Using Amazon S3 Origins and Custom Origins for Web Distributions
A developer is designing a fault-tolerant environment where client sessions will be saved.
How can the developer ensure that no sessions are lost if an Amazon EC2 instance fails?
- A . Use sticky sessions with an Elastic Load Balancer target group.
- B . Use Amazon SOS to save session data.
- C . Use Amazon DynamoDB to perform scalable session handling.
- D . Use Elastic Load Balancer connection draining to stop sending requests to failing instances.
A company has deployed an application on AWS Elastic Beanstalk. The company has configured the Auto Scaling group that is associated with the Elastic Beanstalk environment to have five Amazon EC2 instances. If the capacity is fewer than four EC2 instances during the deployment, application performance degrades. The company is using the all-at-once deployment policy.
What is the MOST cost-effective way to solve the deployment issue?
- A . Change the Auto Scaling group to six desired instances.
- B . Change the deployment policy to traffic splitting. Specify an evaluation time of 1 hour.
- C . Change the deployment policy to rolling with additional batch. Specify a batch size of 1.
- D . Change the deployment policy to rolling. Specify a batch size of 2.
C
Explanation:
This solution will solve the deployment issue by deploying the new version of the application to one new EC2 instance at a time, while keeping the old version running on the existing instances. This way, there will always be at least four instances serving traffic during the deployment, and no downtime or performance degradation will occur.
Option A is not optimal because it will increase the cost of running the Elastic Beanstalk environment without solving the deployment issue.
Option B is not optimal because it will split the traffic between two versions of the application, which may cause inconsistency and confusion for the customers.
Option D is not optimal because it will deploy the
new version of the application to two existing instances at a time, which may reduce the capacity
below four instances during the deployment.
Reference: AWS Elastic Beanstalk Deployment Policies
A company is building a compute-intensive application that will run on a fleet of Amazon EC2 instances. The application uses attached Amazon Elastic Block Store (Amazon EBS) volumes for storing data. The Amazon EBS volumes will be created at time of initial deployment. The application will process sensitive information. All of the data must be encrypted. The solution should not impact the application’s performance.
Which solution will meet these requirements?
- A . Configure the fleet of EC2 instances to use encrypted EBS volumes to store data.
- B . Configure the application to write all data to an encrypted Amazon S3 bucket.
- C . Configure a custom encryption algorithm for the application that will encrypt and decrypt all data.
- D . Configure an Amazon Machine Image (AMI) that has an encrypted root volume and store the data to ephemeral disks.
A
Explanation:
Amazon Elastic Block Store (Amazon EBS) provides block level storage volumes for use with Amazon EC2 instances1. Amazon EBS encryption offers a straight-forward encryption solution for your EBS resources associated with your EC2 instances1. When you create an encrypted EBS volume and attach it to a supported instance type, the following types of data are encrypted: Data at rest inside the volume, all data moving between the volume and the instance, all snapshots created from the volume, and all volumes created from those snapshots1. Therefore, option A is correct.
A company operates a media streaming platform that delivers on-demand video content to users from around the world. User requests flow through an Amazon CloudFront distribution, an Amazon API Gateway REST API, AWS Lambda functions, and Amazon DynamoDB tables.
Some users have reported intermittent buffering issues and delays when users try to start a video stream. The company needs to investigate the issues to discover the underlying cause.
Which solution will meet this requirement?
- A . Enable AWS X-Ray tracing for the REST API, Lambda functions, and DynamoDB tables. Analyze the service map to identify any performance bottlenecks or errors.
- B . Enable logging in API Gateway. Ensure that each Lambda function is configured to send logs to Amazon CloudWatch. Use CloudWatch Logs Insights to query the log data.
- C . Use AWS Config to review details of any recent configuration changes to AWS resources in the application that could result in increased latency for users.
- D . Use AWS CloudTrail to track AWS resources in all AWS Regions. Stream CloudTrail data to an Amazon CloudWatch Logs log group. Enable CloudTrail Insights. Set up Amazon SN5 notifications if unusual API activity is detected.
A
Explanation:
Requirement Summary:
Users experience buffering/delay when starting video stream
Architecture:
CloudFront → API Gateway → Lambda → DynamoDB
Need to identify root cause of performance issues
Evaluate Options:
✅ A: Enable AWS X-Ray tracing
✅ Ideal for end-to-end tracing
Visualizes latency across services (API Gateway, Lambda, DynamoDB) Creates a service map for easy identification of bottlenecks or errors Designed specifically for distributed tracing and performance monitoring
B: CloudWatch Logs Insights
⚠️ Helpful for querying logs
But lacks the visual trace linkage across services like X-Ray Does not identify where latency accumulates C: AWS Config
❌ Tracks configuration changes, not runtime performance D: CloudTrail + CloudWatch Logs
❌ More useful for audit/logging, not tracing performance or latency issues
X-Ray overview: https://docs.aws.amazon.com/xray/latest/devguide/aws-xray.html
Service map: https://docs.aws.amazon.com/xray/latest/devguide/xray-console-service-map.html Tracing API Gateway: https://docs.aws.amazon.com/apigateway/latest/developerguide/apigateway-xray.html
A developer is creating an AWS Lambda function. The Lambda function needs an external library to connect to a third-party solution The external library is a collection of files with a total size of 100 MB The developer needs to make the external library available to the Lambda execution environment and reduce the Lambda package space
Which solution will meet these requirements with the LEAST operational overhead?
- A . Create a Lambda layer to store the external library Configure the Lambda function to use the layer
- B . Create an Amazon S3 bucket Upload the external library into the S3 bucket. Mount the S3 bucket folder in the Lambda function Import the library by using the proper folder in the mount point.
- C . Load the external library to the Lambda function’s /tmp directory during deployment of the Lambda package. Import the library from the /tmp directory.
- D . Create an Amazon Elastic File System (Amazon EFS) volume. Upload the external library to the EFS volume Mount the EFS volume in the Lambda function. Import the library by using the proper folder in the mount point.
A
Explanation:
Lambda Layers: These are designed to package dependencies that you can share across functions.
How to Use:
Create a layer, upload your 100MB library as a zip.
Attach the layer to your function.
In your function code, import the library from the standard layer path.
Reference: Lambda Layers: https://docs.aws.amazon.com/lambda/latest/dg/configuration-layers.html
A developer is receiving HTTP 400: ThrottlingException errors intermittently when calling the Amazon CloudWatch API. When a call fails, no data is retrieved.
What best practice should first be applied to address this issue?
- A . Contact AWS Support for a limit increase.
- B . Use the AWS CLI to get the metrics.
- C . Analyze the applications and remove the API call.
- D . Retry the call with exponential backoff.
A company has an application that consists of different microservices that run inside an AWS account. The microservices are running in containers inside a single VPC. The number of microservices is constantly increasing. A developer must create a central logging solution for application logs.
- A . Create a different Amazon CloudWatch Logs stream for each microservice.
- B . Create an AWS CloudTrail trail to log all the API calls.
- C . Configure VPC Flow Logs to track the communications between the microservices.
- D . Use AWS Cloud Map to map the interactions of the microservices.
A
Explanation:
To create a central logging solution for microservices, using Amazon CloudWatch Logs is a recommended and effective approach. Here’s why:
Amazon CloudWatch Logs Streams allow you to centralize logs from different services, which is crucial as the number of microservices increases.
Each microservice can have its own dedicated log stream within Amazon CloudWatch Logs, providing clear segregation of logs while still allowing centralized management.
This setup enables developers to monitor, search, and analyze logs efficiently using tools like CloudWatch Insights.
Other options like CloudTrail (B) are designed for API activity monitoring, not application logs. VPC
Flow Logs (C) focus on network traffic rather than application behavior. AWS Cloud Map (D) is for
service discovery and routing, not logging.
Reference: AWS CloudWatch Logs documentation