Amazon DVA-C02 Practice Exams
Last updated on Oct 21,2026- Exam Code: DVA-C02
- Exam Name: AWS Certified Developer - Associate
- Certification Provider: Amazon
- Latest update: Oct 21,2026
A developer is building an ecommerce application that uses multiple AWS Lambda functions. Each function performs a specific step in a customer order workflow, such as order processing and inventory management.
The developer must ensure that the Lambda functions run in a specific order.
Which solution will meet this requirement with the LEAST operational overhead?
- A . Configure an Amazon Simple Queue Service (Amazon SQS) queue to contain messages about each step a function must perform. Configure the Lambda functions to run sequentially based on the order of messages in the SQS queue.
- B . Configure an Amazon Simple Notification Service (Amazon SNS) topic to contain notifications about each step a function must perform. Subscribe the Lambda functions to the SNS topic. Use subscription filters based on the step each function must perform.
- C . Configure an AWS Step Functions state machine to invoke the Lambda functions in a specific order.
- D . Configure Amazon EventBridge Scheduler schedules to invoke the Lambda functions in a specific order.
C
Explanation:
The requirement here is to ensure that Lambda functions are executed in a specific order. AWS Step Functions is a low-code workflow orchestration service that enables you to sequence AWS services, such as AWS Lambda, into workflows. It is purpose-built for situations like this, where different steps need to be executed in a strict sequence.
AWS Step Functions: Step Functions allows developers to design workflows as state machines, where each state corresponds to a particular function. In this case, the developer can create a Step Functions state machine where each step (order processing, inventory management, etc.) is represented by a Lambda function.
Operational Overhead: Step Functions have very low operational overhead because it natively handles retries, error handling, and function sequencing.
Alternatives:
Amazon SQS (Option A): While SQS can manage message ordering, it requires more manual handling of each step and the logic to sequentially invoke the Lambda functions.
Amazon SNS (Option B): SNS is a pub/sub service and is not designed to handle sequences of Lambda executions.
EventBridge (Option D): EventBridge Scheduler allows you to invoke Lambda functions based on scheduled times, but it doesn’t directly support sequencing based on workflow logic. Therefore, AWS Step Functions is the most appropriate solution due to its native orchestration capabilities and minimal operational complexity.
Reference: AWS Step Functions documentation
A developer needs to troubleshoot an AWS Lambda function in a development environment. The Lambda function is configured in VPC mode and needs to connect to an existing Amazon RDS for SOL Server DB instance. The DB instance is deployed in a private subnet and accepts connections by using port 1433.
When the developer tests the function, the function reports an error when it tries to connect to the database.
Which combination of steps should the developer take to diagnose this issue? (Select TWO.)
- A . Check that the function’s security group has outbound access on port 1433 to the DB instance’s security group. Check that the DB instance’s security group has inbound access on port 1433 from the function’s security group.
- B . Check that the function’s security group has Inbound access on port 1433 from the DB Instance’s security group. Check that the DB instance’s security group has outbound access on port 1433 to the function’s security group.
- C . Check that the VPC is set up for a NAT gateway. Check that the DB instance has the public access option turned on.
- D . Check that the function’s execution role permissions include rds:DescribeDBInstances, rds: ModifyDB Instance, and rds:DescribeDBSecurityGroups for the DB instance.
- E . Check that the function’s execution rote permissions include ec2: CreateNetworklnterface. ec2: DescribeNetworklnterfaces. and ec2: DeleteNetworklnterface.
A company uses an AWS Lambda function to perform natural language processing (NLP) tasks. The company has attached a Lambda layer to the function. The Lambda layer contain scientific libraries that the function uses during processing.
The company added a large, pre-trained text-classification model to the Lambda layer. The addition increased the size of the Lambda layer to 8.7 GB. After the addition and a recent deployment, the Lambda function returned a RequestEntityTooLargeException error.
The company needs to update the Lambda function with a high-performing and portable solution to
decrease the initialization time for the function.
Which solution will meet these requirements?
- A . Store the large pre-trained model in an Amazon S3 bucket. Use the AWS SDK to access the model.
- B . Create an Amazon EFS file system to store the large pre-trained model. Mount the file system to an Amazon EC2 instance. Configure the Lambda function to use the EFS file system.
- C . Split the components of the Lambda layer into five new Lambda layers. Zip the new layers, and attach the layers to the Lambda function. Update the function code to use the new layers.
- D . Create a Docker container that includes the scientific libraries and the pre-trained model. Update the Lambda function to use the container image.
D
Explanation:
Requirement Summary:
NLP Lambda function with a large pre-trained model
Lambda layer became 8.7 GB → Exceeds AWS limits
Function returns RequestEntityTooLargeException
Need: High-performing, portable, low initialization time
Important AWS Limits:
Lambda Layers size limit (combined across all layers): 250 MB (unzipped)
Deployment package size (unzipped): 250 MB
Lambda container image support allows up to 10 GB image size
Evaluate Options:
A: Store model in S3 and load during execution
❌ Leads to cold start latency every time
Model loading from S3 is slower and not suitable for real-time NLP
Not optimal for performance
B: Use EFS mounted to Lambda
⚠️ Valid for large models, but adds latency during cold start as model loads from EFS Requires EFS setup, VPC, and has added network I/O overhead Still slower than bundling in container image
C: Split into five Lambda layers
❌ Still violates the total layer size limit of 250 MB (unzipped) You cannot exceed that even with multiple layers
✅ D: Use Docker container image
✅ Allows bundling up to 10 GB of dependencies and models
✅ High portability and performance
✅ Avoids latency of downloading models at runtime
✅ Ideal for scientific/NLP models
Lambda container image support: https://docs.aws.amazon.com/lambda/latest/dg/images-create.html
Lambda limits: https://docs.aws.amazon.com/lambda/latest/dg/gettingstarted-limits.html
Using large models with Lambda: https://aws.amazon.com/blogs/machine-learning/deploying-large-machine-learning-models-on-aws-lambda-with-container-images/
A developer received the following error message during an AWS CloudFormation deployment:
Which action should the developer take to resolve this error?
- A . Contact AWS Support to report an issue with the Auto Scaling Groups (ASG> service.
- B . Add a DependsOn attribute to the ASGInstanceRole12345678 resource in the CloudFormation template. Then delete the stack.
- C . Modify the CloudFormation template to retain the ASGInstanceRolet 2345678 resource. Then manually delete the resource after deployment.
- D . Add a force parameter when calling CloudFormation with the role-am of ASGInstanceRole12345678.
A company hosts a stateless web application with low data storage in a single AWS Region. The company wants to increase the resiliency of the application to include a multi-Region presence. The company wants to set the recovery time objective (RTO) and recovery point objective (RPO) to hours. The company needs a low-cost and low-complexity disaster recovery (DR) strategy.
Which DR strategy should the company use?
- A . Warm standby
- B . Pilot light
- C . Backup and restore
- D . Multi-site active-active
B
Explanation:
Why Option B is Correct:
The pilot light strategy keeps a minimal version of the environment in another Region and scales up during a disaster. It achieves an RTO and RPO of hours at a low cost and complexity.
Why Other Options are Incorrect:
Option A: Warm standby is more expensive as it keeps a scaled-down, fully functioning version running in another Region.
Option C: Backup and restore has a longer RTO and RPO than hours.
Option D: Multi-site active-active is costly and more complex than required.
AWS Documentation
Reference: Disaster Recovery Strategies on AWS
A developer is automating a new application deployment with AWS SAM. The new application has one AWS Lambda function and one Amazon S3 bucket. The Lambda function must access the S3 bucket to only read objects.
How should the developer configure AWS SAM to grant the necessary read permission to the S3 bucket?
- A . Reference a second Lambda authorizer function.
- B . Add a custom S3 bucket policy to the Lambda function.
- C . Create an Amazon SQS topic for only S3 object reads. Reference the topic in the template.
- D . Add the S3ReadPolicy template to the Lambda function’s execution role.
D
Explanation:
Step-by-Step Breakdown:
Requirement Summary:
Use AWS SAM to deploy:
1 Lambda Function
1 S3 Bucket
Lambda needs read-only access to the S3 bucket
Solution must be expressed via AWS SAM template
Option A: Reference a second Lambda authorizer function
❌ Incorrect: Lambda authorizers are used in API Gateway for authentication, not for granting S3 permissions.
Option B: Add a custom S3 bucket policy to the Lambda function
❌ Incorrect: Bucket policies control who can access the bucket, not what the Lambda function can
do.
The permission must be granted to the Lambda’s IAM execution role.
Option C: Create an Amazon SQS topic for only S3 object reads
❌ Incorrect: SQS cannot read objects from S3 and is not relevant to this scenario.
Option D: Add the S3ReadPolicy template to the Lambda function’s execution role
✅ Correct: AWS SAM provides managed policy templates like AmazonS3ReadOnlyAccess and shortcuts like S3ReadPolicy.
You can apply these to the Lambda’s execution role using the Policies: section in your SAM template. Example SAM YAML:
yaml CopyEdit Resources: MyFunction:
Type: AWS::Serverless::Function Properties:
CodeUri: my-code/
Handler: app.handler
Runtime: python3.11 Policies:
– S3ReadPolicy:
BucketName: !Ref MyBucket
MyBucket:
Type: AWS::S3::Bucket
Properties:
BucketName: my-personal-bucket-name
SAM Policy Templates: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-policy-templates.html
Example using S3ReadPolicy: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-policy-templates.html#s3-readpolicy
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 . All at once
- B . Rolling with additional batch
- C . Bluegreen
- D . Immutable
B
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.
Reference: AWS Elastic Beanstalk Deployment Policies
A healthcare company uses AWS Amplify to host a patient management system. The system uses Amazon API Gateway to expose RESTful APIs. The backend logic of the system is handled by AWS Lambda functions.
One of the Lambda functions receives patient data that includes personally identifiable information (PII). The Lambda function sends the patient data to an Amazon DynamoDB table. The company must encrypt all patient data at rest and in transit before the data is stored in DynamoDB.
- A . Configure the Lambda function to use AWS KMS keys with the AWS Database Encryption SDK to encrypt the patient data before sending the data to DynamoDB.
- B . Use AWS managed AWS KMS keys to encrypt the data in the DynamoDB table.
- C . Configure a DynamoDB stream on the table to invoke a Lambda function. Configure the Lambda function to use an AWS KMS key to encrypt the DynamoDB table and to update the table.
- D . Use an AWS Step Functions workflow to transfer the data to an Amazon SQS queue. Configure a Lambda function to encrypt the data in the queue before sending the data to the DynamoDB table.
A
Explanation:
Why Option A is Correct:
Encrypting PII at rest and in transit before storing it in DynamoDB ensures end-to-end security. Using the AWS Database Encryption SDK with KMS keys allows the Lambda function to encrypt data before transmission, meeting security and compliance requirements.
Why Other Options are Incorrect:
Option B: While AWS-managed KMS keys encrypt DynamoDB data at rest, they do not encrypt data in transit.
Option C: DynamoDB streams process updates after the data is written to the table, failing to encrypt
PII in transit.
Option D: Step Functions and SQS add unnecessary complexity and still require encryption logic for both transit and at rest.
AWS Documentation
Reference: Encrypting Data in DynamoDB
AWS Database Encryption SDK
A company is using an AWS Lambda function to process records from an Amazon Kinesis data stream. The company recently observed slow processing of the records. A developer notices that the iterator age metric for the function is increasing and that the Lambda run duration is constantly above normal.
Which actions should the developer take to increase the processing speed? (Choose two.)
- A . Increase the number of shards of the Kinesis data stream.
- B . Decrease the timeout of the Lambda function.
- C . Increase the memory that is allocated to the Lambda function.
- D . Decrease the number of shards of the Kinesis data stream.
- E . Increase the timeout of the Lambda function.
A, C
Explanation:
Increasing the number of shards of the Kinesis data stream will increase the throughput and parallelism of the data processing. Increasing the memory that is allocated to the Lambda function will also increase the CPU and network performance of the function, which will reduce the run duration and improve the processing speed.
Option B is not correct because decreasing the timeout of the Lambda function will not affect the processing speed, but may cause some records to fail if they exceed the timeout limit.
Option D is not correct because decreasing the number of shards of the Kinesis data stream will decrease the throughput and parallelism of the data processing, which will slow down the processing speed.
Option E is not correct because increasing the timeout of the Lambda function will not affect the processing speed, but may increase the cost of running the function.
Reference: [Amazon Kinesis Data Streams Scaling], [AWS Lambda Performance Tuning]
A company stores its data in data tables in a series of Amazon S3 buckets. The company received an alert that customer credit card information might have been exposed in a data table on one of the company’s public applications. A developer needs to identify all potential exposures within the application environment.
Which solution will meet these requirements?
- A . Use Amazon Athena to run a job on the S3 buckets that contain the affected data. Filter the findings by using the SensitiveData:S30bject/Personal finding type.
- B . Use Amazon Made to run a job on the S3 buckets that contain the affected data. Filter the findings by using the SensitiveData:S30bject/Financial finding type.
- C . Use Amazon Made to run a job on the S3 buckets that contain the affected data. Filter the findings by using the SensitiveData:S30bject/Personal finding type.
- D . Use Amazon Athena to run a job on the S3 buckets that contain the affected data. Filter the findings by using the SensitiveData:S30bject/Financial finding type.
B
Explanation:
Requirement Summary:
Customer credit card data may be exposed
Data is stored in Amazon S3
Developer must identify all exposure risks
Tool to Use:
✅ Amazon Macie is designed to: Automatically scan S3 for sensitive data Detect financial information, PII, credentials, etc.
Finding Type Mapping:
Credit card data maps to: SensitiveData:S3Object/Financial
Evaluate Options: