AWS Certified Developer - Associate (DVA-C02)

Create a release branch from the latest Git commit that will be in the release. Apply fixes to the release branch. Continue developing new features, and merge the features into the main branch. Merge the release branch into the main branch after the release.

Create a Git tag on the latest Git commit that will be in the release. Continue developing new features, and merge the features into the main branch. Apply fixes to the main branch. Update the Git tag for the release to be on the latest commit on the main branch.

Create a release branch from the latest Git commit that will be in the release. Apply fixes to the release branch. Continue developing new features, and merge the features into the main branch. Rebase the main branch onto the release branch after the release.

Create a Git tag on the latest Git commit that will be in the release. Continue developing new features, and merge the features into the main branch. Apply the Git commits for fixes to the Git tag for the release.

Upload the object to another AWS region

Upload the object using the multi-part upload API

Contact AWS Support to increase the object size limit

Upload the object over an AWS Direct Connect connection

Initialize the database connection outside the handler function. Increase the max_user_connections value on the parameter group of the DB cluster. Restart the DB cluster.

Initialize the database connection outside the handler function. Use RDS Proxy instead of connecting directly to the DB cluster.

Use Amazon Simple Queue Service (Amazon SQS) FIFO queues to queue the orders. Ingest the orders into the database. Set the Lambda function's concurrency to a value that equals the number of available database connections.

Use Amazon Simple Queue Service (Amazon SQS) FIFO queues to queue the orders. Ingest the orders into the database. Set the Lambda function's concurrency to a value that is less than the number of available database connections.

Set up a mock integration for API methods in API Gateway. In the integration request from Method Execution, add simple logic to return either a success or error based on HTTP status code. In the integration response, add messages that correspond to the HTTP status codes.

Create two mock integration resources for API methods in API Gateway. In the integration request, return a success HTTP status code for one resource and an error HTTP status code for the other resource. In the integration response, add messages that correspond to the HTTP status codes.

Create Lambda functions to perform tests. Add simple logic to return either success or error, based on the HTTP status codes. Build an API Gateway Lambda integration. Select appropriate Lambda functions that correspond to the HTTP status codes.

Create a Lambda function to perform tests. Add simple logic to return either success or error-based HTTP status codes. Create a mock integration in API Gateway. Select the Lambda function that corresponds to the HTTP status codes.

Create four access points that allow access to the central S3 bucket. Assign an access point to each web application bucket.

Create a bucket policy that allows access to the central S3 bucket. Attach the bucket policy to the central S3 bucket.

Create a cross-origin resource sharing (CORS) configuration that allows access to the central S3 bucket. Add the CORS configuration to the central S3 bucket.

Create a Content-MD5 header that provides a message integrity check for the central S3 bucket. Insert the Content-MD5 header for each web application request.

Use AWS Amplify for automatic deployment templates. Use a traffic-splitting deployment to copy any deployments. Modify any resources created by Amplify, if necessary.

Use AWS CodeBuild for automatic deployment. Upload the required AppSpec file template. Save the appspec.yml file in the root directory folder of the revision. Specify the deployment group that includes the EC2 instances for the deployment.

Use AWS CloudFormation to create an infrastructure template in JSON format to deploy the EC2 instances. Use CloudFormation helper scripts to install the necessary software and to start the application. Call the scripts directly from the template.

Use AWS AppSync to deploy the application. Upload the template as a GraphQL schema. Specify the EC2 instances for deployment of the application. Use resolvers as a version control mechanism and to make any updates to the deployments.

Update the instance profile role in Account A with stream read permissions.

Create an IAM role with stream read permissions in Account B.

Add a trust policy to the instance profile role and IAM role in Account B to allow the instance profile role to assume the IAM role.

Add a trust policy to the instance profile role and IAM role in Account B to allow reads from the stream.

Add a resource-based policy in Account B to allow read access from the instance profile role.

Modify the application code to perform exponential backoff when the error is received.

Modify the application to use the AWS SDKs for DynamoDB.

Increase the read and write throughput of the DynamoDB table.

Create a DynamoDB Accelerator (DAX) cluster for the DynamoDB table.

Create a second DynamoDB table. Distribute the reads and writes between the two tables.

"codecommit:CreateBranch"
"codecommit:DeleteBranch"

"codecommit:Put*"

"codecommit:Update*"

"codecommit:*"

Enable IAM database authentication on the RDS for MySQL DB instance. Create an IAM role that has the minimum required permissions. Assign the role to the application.

Store the MySQL credentials as secrets in AWS Secrets Manager. Create an IAM role that has the minimum required permissions to retrieve the secrets. Assign the role to the application.

Configure the MySQL credentials as environment variables that are available at runtime for the application.

Store the MySQL credentials as SecureString parameters in AWS Systems Manager Parameter Store.
Create an IAM role that has the minimum required permissions to retrieve the parameters. Assign the role to the application.