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AWS DVA-C01 Exam Certification Details:
| Duration | 130 minutes |
| Exam Name | AWS Developer Associate (AWS-CDA) |
| Exam Code | DVA-C01 |
| Exam Price | $150 USD |
| Number of Questions | 65 |
| Recommended Training / Books | Developing on AWS |
| Passing Score | 720 / 1000 |
| Sample Questions | AWS DVA-C01 Sample Questions |
| Schedule Exam | AWS Certification |
Reference: https://aws.amazon.com/certification/certified-developer-associate/?ch=sec&sec=rmg&d=1
AWS-CDA Exam Syllabus Topics:
| Section | Objectives |
|---|---|
Deployment - 22% | |
| Deploy written code in AWS using existing CI/CD pipelines, processes, and patterns. | - Commit code to a repository and invoke build, test and/or deployment actions - Use labels and branches for version and release management - Use AWS CodePipeline to orchestrate workflows against different environments - Apply AWS CodeCommit, AWS CodeBuild, AWS CodePipeline, AWS CodeStar, and AWS CodeDeploy for CI/CD purposes - Perform a roll back plan based on application deployment policy |
| Deploy applications using AWS Elastic Beanstalk. | - Utilize existing supported environments to define a new application stack - Package the application - Introduce a new application version into the Elastic Beanstalk environment - Utilize a deployment policy to deploy an application version (i.e., all at once, rolling, rolling with batch, immutable) - Validate application health using Elastic Beanstalk dashboard - Use Amazon CloudWatch Logs to instrument application logging |
| Prepare the application deployment package to be deployed to AWS. | - Manage the dependencies of the code module (like environment variables, config files and static image files) within the package - Outline the package/container directory structure and organize files appropriately - Translate application resource requirements to AWS infrastructure parameters (e.g., memory, cores) |
| Deploy serverless applications. | - Given a use case, implement and launch an AWS Serverless Application Model (AWS SAM) template - Manage environments in individual AWS services (e.g., Differentiate between Development, Test, and Production in Amazon API Gateway) |
Security - 26% | |
| Make authenticated calls to AWS services. | - Communicate required policy based on least privileges required by application. - Assume an IAM role to access a service - Use the software development kit (SDK) credential provider on-premises or in the cloud to access AWS services (local credentials vs. instance roles) |
| Implement encryption using AWS services. | - Encrypt data at rest (client side; server side; envelope encryption) using AWS services - Encrypt data in transit |
| Implement application authentication and authorization. | - Add user sign-up and sign-in functionality for applications with Amazon Cognito identity or user pools - Use Amazon Cognito-provided credentials to write code that access AWS services. - Use Amazon Cognito sync to synchronize user profiles and data- Use developer-authenticated identities to interact between end user devices, backend authentication, and Amazon Cognito |
Development with AWS Services - 30% | |
| Write code for serverless applications. | - Compare and contrast server-based vs. serverless model (e.g., micro services, stateless nature of serverless applications, scaling serverless applications, and decoupling layers of serverless applications) - Configure AWS Lambda functions by defining environment variables and parameters (e.g., memory, time out, runtime, handler) - Create an API endpoint using Amazon API Gateway - Create and test appropriate API actions like GET, POST using the API endpoint - Apply Amazon DynamoDB concepts (e.g., tables, items, and attributes) - Compute read/write capacity units for Amazon DynamoDB based on application requirements - Associate an AWS Lambda function with an AWS event source (e.g., Amazon API Gateway, Amazon CloudWatch event, Amazon S3 events, Amazon Kinesis) - Invoke an AWS Lambda function synchronously and asynchronously |
| Translate functional requirements into application design. | - Determine real-time vs. batch processing for a given use case - Determine use of synchronous vs. asynchronous for a given use case - Determine use of event vs. schedule/poll for a given use case - Account for tradeoffs for consistency models in an application design |
| Implement application design into application code. | - Write code to utilize messaging services (e.g., SQS, SNS) - Use Amazon ElastiCache to create a database cache - Use Amazon DynamoDB to index objects in Amazon S3 - Write a stateless AWS Lambda function - Write a web application with stateless web servers (Externalize state) |
| Write code that interacts with AWS services by using APIs, SDKs, and AWS CLI. | - Choose the appropriate APIs, software development kits (SDKs), and CLI commands for the code components - Write resilient code that deals with failures or exceptions (i.e., retries with exponential back off and jitter) |
Refactoring - 10% | |
| Optimize applications to best use AWS services and features. | - Implement AWS caching services to optimize performance (e.g., Amazon ElastiCache, Amazon API Gateway cache) - Apply an Amazon S3 naming scheme for optimal read performance |
| Migrate existing application code to run on AWS. | - Isolate dependencies - Run the application as one or more stateless processes - Develop in order to enable horizontal scalability - Externalize state |
Monitoring and Troubleshooting - 12% | |
| Write code that can be monitored. | - Create custom Amazon CloudWatch metrics - Perform logging in a manner available to systems operators - Instrument application source code to enable tracing in AWS X-Ray |
| Perform root cause analysis on faults found in testing or production. | - Interpret the outputs from the logging mechanism in AWS to identify errors in logs - Check build and testing history in AWS services (e.g., AWS CodeBuild, AWS CodeDeploy, AWS CodePipeline) to identify issues - Utilize AWS services (e.g., Amazon CloudWatch, VPC Flow Logs, and AWS X-Ray) to locate a specific faulty component |
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