AWS Developer Associate Certification Exam Syllabus

• Event source mapping
• Stateless applications
• Unit testing
• Event-driven architecture
• Scalability
• The access of private resources in VPCs from Lambda code

• Configuring Lambda functions by defining environment variables and parameters (for example, memory, concurrency, timeout, runtime, handler, layers, extensions, triggers, destinations)
• Handling the event lifecycle and errors by using code (for example, Lambda Destinations, dead-letter queues)
• Writing and running test code by using AWS services and tools
• Integrating Lambda functions with AWS services
• Tuning Lambda functions for optimal performance

• Relational and non-relational databases
• Create, read, update, and delete (CRUD) operations
• High-cardinality partition keys for balanced partition access
• Cloud storage options (for example, file, object, databases)
• Database consistency models (for example, strongly consistent, eventually consistent)
• Differences between query and scan operations
• Amazon DynamoDB keys and indexing
• Caching strategies (for example, write-through, read-through, lazy loading, TTL)
• Amazon Simple Storage Service (Amazon S3) tiers and lifecycle management
• Differences between ephemeral and persistent data storage patterns

• Serializing and deserializing data to provide persistence to a data store
• Using, managing, and maintaining data stores
• Managing data lifecycles
• Using data caching services

• Identity federation (for example, Security Assertion Markup Language [SAML], OpenID Connect [OIDC], Amazon Cognito)
• Bearer tokens (for example, JSON Web Token [JWT], OAuth, AWS Security Token Service [AWS STS])
• The comparison of user pools and identity pools in Amazon Cognito
• Resource-based policies, service policies, and principal policies
• Role-based access control (RBAC)
• Application authorization that uses ACLs
• The principle of least privilege
• Differences between AWS managed policies and customer-managed policies
• Identity and access management (IAM)

Skills in:

• Using an identity provider to implement federated access (for example, Amazon Cognito, AWS Identity and Access Management [IAM])
• Securing applications by using bearer tokens
• Configuring programmatic access to AWS
• Making authenticated calls to AWS services
• Assuming an IAM role
• Defining permissions for principals

• Encryption at rest and in transit
• Certificate management (for example, AWS Private Certificate Authority)
• Key protection (for example, key rotation)
• Differences between client-side encryption and server-side encryption
• Differences between AWS managed and customer managed AWS Key Management Service (AWS KMS) keys

• Data classification (for example, personally identifiable information [PII], protected health information [PHI])
• Environment variables
• Secrets management (for example, AWS Secrets Manager, AWS Systems Manager Parameter Store)
• Secure credential handling

• Encrypting environment variables that contain sensitive data
• Using secret management services to secure sensitive data
• Sanitizing sensitive data

• Ways to access application configuration data (for example, AWS AppConfig, Secrets Manager, Parameter Store)
• Lambda deployment packaging, layers, and configuration options
• Git-based version control tools (for example, Git, AWS CodeCommit)
• Container images

• Managing the dependencies of the code module (for example, environment variables, configuration files, container images) within the package
• Organizing files and a directory structure for application deployment
• Using code repositories in deployment environments
• Applying application requirements for resources (for example, memory, cores)

• Features in AWS services that perform application deployment
• Integration testing that uses mock endpoints
• Lambda versions and aliases

• API Gateway stages
• Branches and actions in the continuous integration and continuous delivery (CI/CD) workflow
• Automated software testing (for example, unit testing, mock testing)

• Creating application test events (for example, JSON payloads for testing Lambda, API Gateway, AWS SAM resources)
• Deploying API resources to various environments
• Creating application environments that use approved versions for integration testing (for example, Lambda aliases, container image tags, AWS Amplify branches, AWS Copilot environments)
• Implementing and deploying infrastructure as code (IaC) templates (for example, AWS SAM templates, AWS CloudFormation templates)
• Managing environments in individual AWS services (for example, differentiating between development, test, and production in API Gateway)

• Git-based version control tools (for example, Git, AWS CodeCommit)
• Manual and automated approvals in AWS CodePipeline
• Access application configurations from AWS AppConfig and Secrets Manager
• CI/CD workflows that use AWS services
• Application deployment that uses AWS services and tools (for example, CloudFormation, AWS Cloud Development Kit [AWS CDK], AWS SAM, AWS CodeArtifact, AWS Copilot, Amplify, Lambda)
• Lambda deployment packaging options
• API Gateway stages and custom domains
• Deployment strategies (for example, canary, blue/green, rolling)

• Updating existing IaC templates (for example, AWS SAM templates, CloudFormation templates)
• Managing application environments by using AWS services
• Deploying an application version by using deployment strategies
• Committing code to a repository to invoke build, test, and deployment actions
• Using orchestrated workflows to deploy code to different environments
• Performing application rollbacks by using existing deployment strategies
• Using labels and branches for version and release management
• Using existing runtime configurations to create dynamic deployments (for example, using staging variables from API Gateway in Lambda functions)

• Logging and monitoring systems
• Languages for log queries (for example, Amazon CloudWatch Logs Insights)
• Data visualizations
• Code analysis tools
• Common HTTP error codes
• Common exceptions generated by SDKs
• Service maps in AWS X-Ray

• Debugging code to identify defects
• Interpreting application metrics, logs, and traces
• Querying logs to find relevant data
• Implementing custom metrics (for example, CloudWatch embedded metric format [EMF])
• Reviewing application health by using dashboards and insights
• Troubleshooting deployment failures by using service output logs

• Distributed tracing
• Differences between logging, monitoring, and observability
• Structured logging
• Application metrics (for example, custom, embedded, built-in)

• Implementing an effective logging strategy to record application behavior and state
• Implementing code that emits custom metrics
• Adding annotations for tracing services
• Implementing notification alerts for specific actions (for example, notifications about quota limits or deployment completions)
• Implementing tracing by using AWS services and tools

• Caching
• Concurrency
• Messaging services (for example, Amazon Simple Queue Service [Amazon SQS], Amazon Simple Notification Service [Amazon SNS])

• Profiling application performance
• Determining minimum memory and compute power for an application
• Using subscription filter policies to optimize messaging
• Caching content based on request headers