AWS Certified Generative AI Developer - Professional (AIP-C01) Exam Guide

Version 1.0 AIP-C01

Introduction

The AWS Certified Generative AI Developer - Professional (AIP-C01) exam is intended for individuals who perform a developer role. This exam validates a candidate's ability to develop, test, deploy, and troubleshoot generative AI applications by using AWS services and tools.

The exam also validates a candidate's ability to complete the following tasks:

Integrate foundation models (FMs) and manage data for generative AI applications.
Implement and integrate generative AI solutions by using AWS services and tools.
Ensure AI safety, security, and governance for generative AI applications.
Optimize the operational efficiency and performance of generative AI applications.
Test, validate, and troubleshoot generative AI applications.

Target Candidate Description

The target candidate has experience developing and maintaining generative AI applications by using AWS services and tools. The target candidate also has experience with the following areas:

Proficiency in programming languages such as Python or JavaScript/TypeScript
Understanding of generative AI fundamentals, including transformer architectures and FM capabilities
Hands-on experience with AWS AI/ML services, including Amazon Bedrock, Amazon SageMaker, and AWS Lambda
Knowledge of software development best practices, including version control, testing, and CI/CD pipelines
Experience with API development and integration
Understanding of cloud computing concepts and AWS core services

Recommended AWS Knowledge

The target candidate should have the following knowledge and experience:

2 or more years of experience developing applications on AWS
Hands-on experience with Amazon Bedrock or Amazon SageMaker for generative AI development
Experience with AWS SDKs (for example, AWS SDK for Python [Boto3]) and AWS CLI for programmatic interactions
Knowledge of containerization and serverless technologies on AWS
Understanding of networking concepts in AWS (for example, Amazon VPC, security groups, AWS PrivateLink)
Familiarity with monitoring and logging services (for example, Amazon CloudWatch, AWS CloudTrail)

Exam Content

Question Types

The exam contains one or more of the following question types:

Multiple choice: Has one correct response and three incorrect responses (distractors).
Multiple response: Has two or more correct responses out of five or more response options.

Unanswered questions are scored as incorrect; there is no penalty for guessing. The exam includes 65 questions that affect your score.

Unscored Content

The exam includes 20 unscored questions that do not affect your score. AWS collects information about performance on these unscored questions to evaluate these questions for future use as scored questions. These unscored questions are not identified on the exam.

Exam Results

The AWS Certified Generative AI Developer - Professional (AIP-C01) exam has a pass or fail designation. The exam is scored against a minimum standard established by AWS professionals who follow certification industry best practices and guidelines.

Your results for the exam are reported as a scaled score of 100-1,000. The minimum passing score is 750. Your score shows how you performed on the exam as a whole and whether you passed. Scaled scoring models help equate scores across multiple exam forms that might have slightly different difficulty levels.

Your score report could contain a table of classifications of your performance at each section level. The exam uses a compensatory scoring model, which means that you do not need to achieve a passing score in each section. You need to pass only the overall exam.

Each section of the exam has a specific weighting, so some sections have more questions than other sections have. The table of classifications contains general information that highlights your strengths and weaknesses. Use caution when you interpret section-level feedback.

Content Outline

This exam guide includes weightings, content domains, and task statements for the exam. This guide does not provide a comprehensive list of the content on the exam. However, additional context for each task statement is available to help you prepare for the exam.

The exam has the following content domains and weightings:

Domain 1: Foundation Model Integration, Data Management, and Compliance (31% of scored content)
Domain 2: Implementation and Integration (26% of scored content)
Domain 3: AI Safety, Security, and Governance (20% of scored content)
Domain 4: Operational Efficiency and Optimization for GenAI Applications (12% of scored content)
Domain 5: Testing, Validation, and Troubleshooting (11% of scored content)

Domain 1: Foundation Model Integration, Data Management, and Compliance

Task Statement 1.1: Analyze requirements and design generative AI solutions.

Knowledge of:

Generative AI application architecture patterns (for example, multi-tier architectures, microservices, event-driven architectures)
AWS services for generative AI application development (for example, Amazon Bedrock, Amazon SageMaker, AWS Lambda, Amazon ECS, Amazon EKS)
Design considerations for generative AI applications (for example, scalability, availability, performance, cost, security)
Data flow patterns for generative AI applications (for example, synchronous, asynchronous, streaming, batch)
Integration patterns for generative AI applications (for example, API Gateway, message queues, event buses)

Skills in:

Translating business requirements into technical architectures for generative AI solutions
Selecting appropriate AWS services and features for generative AI application components
Designing data flow and integration patterns for generative AI applications
Evaluating trade-offs between different architectural approaches (for example, cost vs. performance, latency vs. throughput)
Designing solutions that meet non-functional requirements (for example, security, compliance, availability)

Task Statement 1.2: Select and configure foundation models.

Knowledge of:

FM types and their capabilities (for example, text generation, code generation, image generation, multi-modal models, embedding models)
FM selection criteria (for example, model size, latency, cost, accuracy, context window, language support, customization options)
Amazon Bedrock model providers and their available models (for example, Anthropic Claude, Amazon Titan, Meta Llama, Cohere, Stability AI, Mistral AI)
Model customization techniques (for example, fine-tuning, continued pre-training, prompt engineering, Retrieval Augmented Generation [RAG])
Model inference parameters and their effects (for example, temperature, top_p, top_k, max tokens, stop sequences)
Model versioning and lifecycle management

Skills in:

Evaluating and selecting FMs based on use case requirements
Configuring model inference parameters to optimize output quality
Implementing model access and permissions by using Amazon Bedrock or Amazon SageMaker
Comparing model performance across different providers and model families
Configuring model throughput and provisioned capacity
Managing model versions and updates

Task Statement 1.3: Implement data validation and processing pipelines for FM consumption.

Knowledge of:

Data preprocessing techniques for FM inputs (for example, text cleaning, tokenization, normalization, chunking strategies)
Data validation methods and frameworks (for example, schema validation, data quality checks, input sanitization)
Data transformation patterns for different FM input types (for example, text, images, structured data)
AWS services for data processing (for example, AWS Lambda, AWS Glue, Amazon S3, AWS Step Functions)
Data format requirements for different FMs and APIs
Chunking strategies and their trade-offs (for example, fixed-size, semantic, recursive, document-based)

Skills in:

Building data preprocessing pipelines for FM inputs
Implementing data validation and quality checks
Transforming data into formats suitable for FM consumption
Implementing error handling and retry logic for data pipelines
Optimizing data processing for cost and performance
Implementing data versioning and lineage tracking

Task Statement 1.4: Design and implement vector store solutions.

Knowledge of:

Vector database concepts (for example, embeddings, similarity search, distance metrics, indexing algorithms)
AWS services for vector storage (for example, Amazon OpenSearch Service, Amazon Aurora PostgreSQL with pgvector, Amazon Neptune, Amazon DocumentDB, Amazon MemoryDB)
Embedding model selection and configuration (for example, Amazon Titan Embeddings, Cohere Embed)
Vector indexing strategies (for example, HNSW, IVF, flat indexing) and their trade-offs
Dimensionality considerations and their impact on performance and storage
Vector store scaling and performance optimization

Skills in:

Selecting and configuring appropriate vector store solutions based on requirements
Generating and managing embeddings by using embedding models
Implementing vector indexing and search configurations
Optimizing vector store performance (for example, index tuning, sharding, caching)
Implementing vector store data ingestion pipelines
Managing vector store lifecycle operations (for example, updates, deletions, re-indexing)

Task Statement 1.5: Design retrieval mechanisms for FM augmentation.

Knowledge of:

RAG architecture patterns and components (for example, retrieval, augmentation, generation)
Amazon Bedrock Knowledge Bases configuration and management
Retrieval strategies (for example, semantic search, keyword search, hybrid search)
Context window management and optimization
Relevance scoring and re-ranking methods
Data source connectors and integration patterns (for example, Amazon S3, web crawlers, databases, Confluence, SharePoint)

Skills in:

Designing and implementing RAG solutions by using Amazon Bedrock Knowledge Bases
Configuring data source connectors and sync schedules
Implementing retrieval strategies that optimize relevance and accuracy
Managing context window utilization for effective augmentation
Implementing metadata filtering and search refinement
Evaluating and tuning retrieval performance

Task Statement 1.6: Implement prompt engineering strategies and governance for FM interactions.

Knowledge of:

Prompt engineering techniques (for example, zero-shot, few-shot, chain-of-thought, role-based, system prompts)
Prompt templates and parameterization strategies
Prompt governance and management best practices
Prompt versioning and lifecycle management
Prompt injection attacks and mitigation strategies
Amazon Bedrock prompt management features (for example, prompt flows, prompt versions)

Skills in:

Designing and implementing effective prompts for different use cases
Creating reusable prompt templates with parameterization
Implementing prompt governance frameworks (for example, review processes, approval workflows)
Managing prompt versions and deployments
Testing and evaluating prompt effectiveness
Implementing prompt guardrails and safety measures

Domain 2: Implementation and Integration

Task Statement 2.1: Implement agentic AI solutions and tool integrations.

Knowledge of:

Agentic AI concepts and architectures (for example, autonomous agents, multi-agent systems, tool use, reasoning loops)
Amazon Bedrock Agents configuration and management (for example, action groups, knowledge bases, guardrails)
Tool and API integration patterns for agents (for example, AWS Lambda functions, API definitions, OpenAPI schemas)
Agent orchestration strategies (for example, sequential, parallel, hierarchical)
Agent memory and state management
Multi-agent collaboration patterns

Skills in:

Designing and implementing AI agents by using Amazon Bedrock Agents
Configuring agent action groups and tool integrations
Implementing agent orchestration workflows
Managing agent state and conversation context
Integrating agents with external APIs and services
Testing and debugging agent behavior

Task Statement 2.2: Implement model deployment strategies.

Knowledge of:

Model deployment options on AWS (for example, Amazon Bedrock on-demand, provisioned throughput, Amazon SageMaker endpoints)
Deployment strategies (for example, blue/green, canary, rolling, A/B testing)
Model hosting configurations (for example, instance types, auto-scaling, multi-model endpoints)
Containerization for model deployment (for example, custom inference containers, Amazon ECR)
Serverless deployment options (for example, AWS Lambda, Amazon Bedrock)
Model deployment pipeline automation (for example, AWS CodePipeline, AWS CodeBuild)

Skills in:

Deploying models by using Amazon Bedrock and Amazon SageMaker
Configuring auto-scaling for model endpoints
Implementing deployment strategies for model updates
Building CI/CD pipelines for model deployment
Managing model deployment configurations and environments
Monitoring deployment health and rollback procedures

Task Statement 2.3: Design and implement enterprise integration architectures.

Knowledge of:

Enterprise integration patterns for generative AI (for example, API Gateway patterns, event-driven architectures, message queuing)
AWS integration services (for example, Amazon API Gateway, Amazon EventBridge, Amazon SQS, Amazon SNS, AWS Step Functions)
Authentication and authorization patterns (for example, Amazon Cognito, IAM, API keys, OAuth)
Rate limiting and throttling strategies
Caching strategies for generative AI applications (for example, Amazon ElastiCache, Amazon CloudFront)
Cross-account and cross-region architectures

Skills in:

Designing API architectures for generative AI applications
Implementing authentication and authorization for API access
Configuring rate limiting and throttling for API endpoints
Implementing caching strategies to optimize performance and reduce costs
Building event-driven architectures for asynchronous generative AI workflows
Implementing cross-account and cross-region access patterns

Task Statement 2.4: Implement FM API integrations.

Knowledge of:

Amazon Bedrock APIs (for example, InvokeModel, InvokeModelWithResponseStream, Converse, ConverseStream)
AWS SDKs for generative AI development (for example, AWS SDK for Python [Boto3], AWS SDK for JavaScript)
API request and response formats for different model providers
Streaming response handling patterns
Error handling and retry strategies for FM APIs
Token management and usage tracking

Skills in:

Implementing FM API calls by using AWS SDKs
Handling streaming responses from FM APIs
Implementing error handling, retry logic, and circuit breakers
Managing API authentication and credentials
Implementing token usage tracking and management
Optimizing API calls for latency and throughput

Task Statement 2.5: Implement application integration patterns and development tools.

Knowledge of:

Generative AI application development frameworks and libraries (for example, LangChain, LlamaIndex, Amazon Bedrock SDK)
Application integration patterns (for example, chatbots, document processing, code generation, content creation)
Conversation management patterns (for example, session management, conversation history, context management)
AWS development tools for generative AI (for example, Amazon Q Developer, AWS Cloud9, AWS CloudShell)
Infrastructure as code (IaC) for generative AI applications (for example, AWS CloudFormation, AWS CDK)
Logging and observability integration (for example, Amazon CloudWatch, AWS X-Ray)

Skills in:

Building generative AI applications by using frameworks and libraries
Implementing conversation management and session handling
Integrating generative AI capabilities into existing applications
Using IaC to deploy generative AI infrastructure
Implementing logging and observability for generative AI applications
Using AWS development tools for generative AI application development

Domain 3: AI Safety, Security, and Governance

Task Statement 3.1: Implement input and output safety controls.

Knowledge of:

Input validation and sanitization techniques for generative AI (for example, prompt injection detection, content filtering, input length validation)
Output safety controls (for example, content filtering, toxicity detection, PII detection, hallucination mitigation)
Amazon Bedrock Guardrails configuration and management (for example, content filters, denied topics, word filters, sensitive information filters, contextual grounding)
Safety testing methodologies (for example, red teaming, adversarial testing, boundary testing)
Content moderation services and techniques (for example, Amazon Comprehend, Amazon Rekognition for image moderation)

Skills in:

Configuring Amazon Bedrock Guardrails for content safety
Implementing input validation and sanitization pipelines
Implementing output filtering and post-processing for safety
Designing and executing safety testing procedures
Implementing content moderation workflows
Monitoring and responding to safety violations

Task Statement 3.2: Implement data security and privacy controls.

Knowledge of:

Data encryption at rest and in transit for generative AI applications (for example, AWS KMS, TLS/SSL)
Data access control mechanisms (for example, IAM policies, resource-based policies, VPC endpoints, AWS PrivateLink)
PII detection and handling (for example, Amazon Comprehend PII detection, data masking, tokenization)
Data residency and sovereignty requirements
Secure data storage patterns for generative AI (for example, encrypted S3 buckets, encrypted vector stores)
Network security for generative AI applications (for example, VPC configurations, security groups, network ACLs)

Skills in:

Implementing encryption for data at rest and in transit
Configuring access controls for generative AI resources and data
Implementing PII detection and redaction pipelines
Configuring network security for generative AI applications
Implementing data lifecycle management and retention policies
Configuring VPC endpoints and AWS PrivateLink for private access to AI services

Task Statement 3.3: Implement AI governance and compliance mechanisms.

Knowledge of:

AI governance frameworks and best practices (for example, model cards, documentation requirements, audit trails)
AWS services for governance and compliance (for example, AWS CloudTrail, AWS Config, AWS Audit Manager, Amazon CloudWatch)
Model inventory and lifecycle management
Compliance requirements for AI systems (for example, data privacy regulations, industry-specific requirements)
Logging and auditing requirements for generative AI applications
Change management processes for AI models and applications

Skills in:

Implementing audit trails and logging for generative AI applications
Configuring AWS services for governance and compliance monitoring
Creating and maintaining model documentation (for example, model cards, data sheets)
Implementing compliance controls for data privacy and regulatory requirements
Managing model inventories and lifecycle tracking
Implementing change management processes for AI systems

Task Statement 3.4: Implement responsible AI principles.

Knowledge of:

Responsible AI principles (for example, fairness, transparency, accountability, explainability, safety, privacy)
Bias detection and mitigation techniques (for example, Amazon SageMaker Clarify, data auditing, model evaluation)
Transparency and explainability methods for generative AI (for example, model documentation, output attribution, source citation)
Human-in-the-loop patterns (for example, Amazon Augmented AI [Amazon A2I], review workflows, feedback loops)
Environmental considerations for AI workloads (for example, compute optimization, carbon footprint awareness)

Skills in:

Implementing bias detection and mitigation strategies
Designing transparency and explainability features for generative AI applications
Implementing human-in-the-loop workflows for AI oversight
Designing feedback mechanisms to improve model fairness and accuracy
Implementing source citation and attribution for RAG-based applications
Monitoring and reporting on responsible AI metrics

Domain 4: Operational Efficiency and Optimization for GenAI Applications

Task Statement 4.1: Implement cost optimization and resource efficiency strategies.

Knowledge of:

Cost components of generative AI applications (for example, token-based pricing, compute costs, storage costs, data transfer costs)
Cost optimization strategies for FM usage (for example, model selection, prompt optimization, caching, batching, provisioned throughput)
AWS pricing models for generative AI services (for example, Amazon Bedrock on-demand pricing, provisioned throughput pricing, Amazon SageMaker pricing)
Resource right-sizing for generative AI workloads
AWS cost management tools (for example, AWS Cost Explorer, AWS Budgets, AWS Cost and Usage Reports)

Skills in:

Implementing token usage optimization strategies (for example, prompt compression, response length management)
Configuring provisioned throughput for predictable workloads
Implementing caching strategies to reduce API calls and costs
Monitoring and analyzing cost patterns for generative AI applications
Implementing cost allocation and tagging strategies
Right-sizing compute resources for generative AI workloads

Task Statement 4.2: Optimize application performance.

Knowledge of:

Performance optimization techniques for generative AI applications (for example, caching, batching, streaming, asynchronous processing)
Latency optimization strategies (for example, model selection, inference parameter tuning, edge caching, connection pooling)
Throughput optimization strategies (for example, auto-scaling, load balancing, concurrent request management)
Amazon Bedrock performance features (for example, provisioned throughput, model invocation logging, batch inference)
Amazon SageMaker performance optimization (for example, instance type selection, auto-scaling, multi-model endpoints)

Skills in:

Implementing caching and batching strategies for performance optimization
Configuring auto-scaling for generative AI workloads
Implementing streaming responses for improved user experience
Optimizing model inference parameters for performance
Implementing load balancing and traffic management
Profiling and benchmarking generative AI application performance

Task Statement 4.3: Implement monitoring systems for generative AI applications.

Knowledge of:

Monitoring strategies for generative AI applications (for example, operational metrics, model performance metrics, business metrics)
AWS monitoring services (for example, Amazon CloudWatch, AWS CloudTrail, AWS X-Ray)
Amazon Bedrock monitoring features (for example, model invocation logging, CloudWatch metrics)
Alerting and notification strategies (for example, Amazon CloudWatch alarms, Amazon SNS)
Logging best practices for generative AI applications (for example, structured logging, log aggregation, log retention)
Observability patterns for distributed generative AI systems

Skills in:

Configuring Amazon CloudWatch metrics and dashboards for generative AI applications
Implementing model invocation logging and analysis
Setting up alerting and notification systems for operational issues
Implementing distributed tracing for generative AI applications
Designing monitoring dashboards for operational and business metrics
Implementing log aggregation and analysis workflows

Domain 5: Testing, Validation, and Troubleshooting

Task Statement 5.1: Implement evaluation systems for generative AI.

Knowledge of:

Evaluation methodologies for generative AI (for example, automated evaluation, human evaluation, benchmark datasets)
Evaluation metrics for text generation (for example, ROUGE, BLEU, BERTScore, perplexity, semantic similarity)
Evaluation metrics for RAG systems (for example, retrieval accuracy, answer relevance, faithfulness, context relevance)
Amazon Bedrock evaluation features (for example, model evaluation jobs, human evaluation workflows)
A/B testing and experimentation frameworks for generative AI
Evaluation data management (for example, golden datasets, test suites, evaluation harnesses)

Skills in:

Designing and implementing automated evaluation pipelines
Configuring Amazon Bedrock model evaluation jobs
Implementing human evaluation workflows
Creating and managing evaluation datasets and test suites
Implementing A/B testing for generative AI features
Analyzing evaluation results and making data-driven decisions

Task Statement 5.2: Troubleshoot generative AI applications.

Knowledge of:

Common failure modes in generative AI applications (for example, hallucinations, context window limitations, token limits, rate limiting, timeout errors)
Debugging techniques for generative AI applications (for example, log analysis, trace analysis, prompt debugging, response analysis)
AWS troubleshooting tools and services (for example, Amazon CloudWatch Logs, AWS X-Ray, AWS CloudTrail)
Error handling patterns for generative AI applications (for example, retry strategies, circuit breakers, fallback mechanisms, graceful degradation)
Performance troubleshooting (for example, latency analysis, throughput bottleneck identification, resource utilization analysis)

Skills in:

Diagnosing and resolving common generative AI application issues
Using AWS monitoring and logging tools for troubleshooting
Implementing error handling and recovery mechanisms
Debugging prompt-related issues (for example, unexpected outputs, inconsistent responses)
Troubleshooting integration issues (for example, API errors, authentication failures, network connectivity)
Analyzing and resolving performance bottlenecks

Appendix

Technologies and Concepts That Might Appear on the Exam

The following list contains technologies and concepts that might appear on the exam. This list is non-exhaustive and is subject to change. The order and placement of the items in this list are not indicative of their relative weight or importance on the exam:

Transformer architectures and attention mechanisms
Foundation models (FMs) and large language models (LLMs)
Multi-modal models (for example, text, image, audio, video)
Embedding models and vector representations
Tokenization and token management
Retrieval Augmented Generation (RAG)
Prompt engineering (for example, zero-shot, few-shot, chain-of-thought)
Fine-tuning and continued pre-training
Reinforcement learning from human feedback (RLHF)
Agentic AI and autonomous agents
Tool use and function calling
Vector databases and similarity search
Chunking strategies (for example, fixed-size, semantic, recursive)
Context window management
Streaming inference
Model evaluation metrics (for example, ROUGE, BLEU, BERTScore, perplexity)
Guardrails and content filtering
Prompt injection and adversarial attacks
PII detection and redaction
Model cards and documentation
Responsible AI principles (for example, fairness, transparency, accountability)
CI/CD for generative AI applications
Infrastructure as code (IaC)
Containerization and serverless computing
API design and management
Event-driven architectures
Caching strategies
Cost optimization for token-based pricing
Monitoring and observability
A/B testing and experimentation

In-scope AWS Services and Features

The following list contains AWS services and features that are in scope for the exam. This list is non-exhaustive and is subject to change. AWS offerings appear in categories that align with the offerings' primary functions:

Analytics:

Amazon Athena
Amazon Kinesis
Amazon OpenSearch Service
AWS Glue

Compute:

Amazon EC2
Amazon EC2 Auto Scaling
AWS Lambda

Containers:

Amazon Elastic Container Registry (Amazon ECR)
Amazon Elastic Container Service (Amazon ECS)
Amazon Elastic Kubernetes Service (Amazon EKS)
AWS Fargate

Database:

Amazon Aurora
Amazon DocumentDB (with MongoDB compatibility)
Amazon DynamoDB
Amazon ElastiCache
Amazon MemoryDB
Amazon Neptune
Amazon RDS

Developer Tools:

AWS Cloud Development Kit (AWS CDK)
AWS CloudFormation
AWS CodeBuild
AWS CodePipeline
Amazon Q Developer

Machine Learning:

Amazon Augmented AI (Amazon A2I)
Amazon Bedrock
Amazon Bedrock Agents
Amazon Bedrock Guardrails
Amazon Bedrock Knowledge Bases
Amazon Comprehend
Amazon Rekognition
Amazon SageMaker
Amazon Textract
Amazon Transcribe
Amazon Translate

Management and Governance:

AWS CloudTrail
Amazon CloudWatch
Amazon CloudWatch Logs
AWS Config
Amazon EventBridge
AWS Systems Manager
AWS Trusted Advisor
AWS Well-Architected Tool
AWS X-Ray

Networking and Content Delivery:

Amazon API Gateway
Amazon CloudFront
Elastic Load Balancing (ELB)
Amazon VPC
AWS PrivateLink

Security, Identity, and Compliance:

AWS Audit Manager
Amazon Cognito
AWS Identity and Access Management (IAM)
AWS Key Management Service (AWS KMS)
Amazon Macie
AWS Secrets Manager
AWS Security Token Service (AWS STS)
AWS WAF

Storage:

Amazon Elastic Block Store (Amazon EBS)
Amazon Elastic File System (Amazon EFS)
Amazon S3
Amazon S3 Glacier

Application Integration:

Amazon Simple Notification Service (Amazon SNS)
Amazon Simple Queue Service (Amazon SQS)
AWS Step Functions

Out-of-scope AWS Services and Features

The following list contains AWS services and features that are out of scope for the exam. This list is non-exhaustive and is subject to change. AWS offerings that are entirely unrelated to the target job roles for the exam are excluded from this list:

Analytics:

AWS Clean Rooms
Amazon CloudSearch
AWS Data Exchange
Amazon Data Firehose
Amazon FinSpace
AWS Lake Formation
Amazon Managed Streaming for Apache Kafka (Amazon MSK)
Amazon QuickSight
Amazon Redshift

Application Integration:

Amazon AppFlow
Amazon MQ
Amazon Simple Workflow Service (Amazon SWF)

Business Applications:

Amazon Chime
Amazon Connect
Amazon Pinpoint
Amazon Simple Email Service (Amazon SES)
AWS Supply Chain
AWS Wickr
Amazon WorkDocs
Amazon WorkMail

Cloud Financial Management:

AWS Application Cost Profiler
AWS Billing Conductor
AWS Budgets
AWS Cost Explorer
AWS Marketplace

Compute:

AWS App Runner
AWS Batch
AWS Elastic Beanstalk
EC2 Image Builder
Amazon Lightsail

Containers:

Red Hat OpenShift Service on AWS (ROSA)

Customer Enablement:

AWS IQ
AWS Managed Services (AMS)
AWS re:Post Private
AWS Support

Database:

Amazon Keyspaces (for Apache Cassandra)
Amazon Quantum Ledger Database (Amazon QLDB)
Amazon Timestream

Developer Tools:

AWS AppConfig
AWS Application Composer
AWS Cloud9
AWS CloudShell
Amazon CodeCatalyst
AWS CodeStar
AWS Fault Injection Service

End User Computing:

Amazon AppStream 2.0
Amazon WorkSpaces
Amazon WorkSpaces Thin Client
Amazon WorkSpaces Web

Frontend Web and Mobile:

AWS Amplify
AWS AppSync
AWS Device Farm
Amazon Location Service

Internet of Things (IoT):

AWS IoT Analytics
AWS IoT Core
AWS IoT Device Defender
AWS IoT Device Management
AWS IoT Events
AWS IoT FleetWise
FreeRTOS
AWS IoT Greengrass
AWS IoT SiteWise
AWS IoT TwinMaker

Machine Learning:

Amazon Bedrock Studio
AWS DeepComposer
AWS DeepRacer
Amazon Forecast
Amazon Fraud Detector
AWS HealthImaging
AWS HealthOmics
Amazon Kendra
Amazon Lex
Amazon Lookout for Equipment
Amazon Lookout for Metrics
Amazon Lookout for Vision
Amazon Monitron
AWS Panorama
Amazon Personalize
Amazon Polly
Amazon Q Business

Management and Governance:

AWS Control Tower
AWS Health Dashboard
AWS Launch Wizard
AWS License Manager
Amazon Managed Grafana
Amazon Managed Service for Prometheus
AWS OpsWorks
AWS Organizations
AWS Proton
AWS Resilience Hub
AWS Resource Explorer
AWS Resource Groups
AWS Systems Manager Incident Manager
AWS Service Catalog
Service Quotas

Media:

Amazon Elastic Transcoder
AWS Elemental MediaConnect
AWS Elemental MediaConvert
AWS Elemental MediaLive
AWS Elemental MediaPackage
AWS Elemental MediaStore
AWS Elemental MediaTailor
Amazon Interactive Video Service (Amazon IVS)

Migration and Transfer:

AWS Application Discovery Service
AWS Application Migration Service
AWS Database Migration Service (AWS DMS)
AWS DataSync
AWS Mainframe Modernization
AWS Migration Hub
AWS Snow Family
AWS Transfer Family

Networking and Content Delivery:

AWS App Mesh
AWS Cloud Map
AWS Direct Connect
AWS Global Accelerator
AWS Private 5G
Amazon Route 53
Amazon VPC IP Address Manager (IPAM)

Security, Identity, and Compliance:

AWS Artifact
AWS Certificate Manager (ACM)
AWS CloudHSM
Amazon Detective
AWS Directory Service
AWS Firewall Manager
Amazon GuardDuty
AWS IAM Identity Center
Amazon Inspector
AWS Payment Cryptography
AWS Private Certificate Authority
AWS Resource Access Manager (AWS RAM)
AWS Security Hub
Amazon Security Lake
AWS Shield
AWS Signer
Amazon Verified Permissions

Storage:

AWS Backup
AWS Elastic Disaster Recovery

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