The AWS DVA-C01 exam preparation guide is designed to provide candidates with necessary information about the AWS-CDA exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the AWS Certified Developer - Associate exam.
It is recommended for all the candidates to refer the DVA-C01 objectives and sample questions provided in this preparation guide. The AWS-CDA certification is mainly targeted to the candidates who want to build their career in Developer domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual AWS Developer Associate exam.
AWS DVA-C01 Exam Summary:
|
Exam Name
|
AWS Developer Associate (AWS-CDA) |
| Exam Code | DVA-C01 |
| Exam Price | $150 USD |
| Duration | 130 minutes |
| Number of Questions | 65 |
| Passing Score | 720 / 1000 |
| Recommended Training / Books | Developing on AWS |
| Schedule Exam | AWS Certification |
| Sample Questions | AWS DVA-C01 Sample Questions |
| Recommended Practice | AWS Certified Developer - Associate Practice Test |
AWS-CDA Syllabus:
| 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 |