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AWS Certified Generative AI Developer - Professional (AIP-C01) Syllabus

Foundation Model Integration, Data Management, and Compliance

Designing generative AI solutions, selecting foundation models, preparing data, and building vector stores, retrieval and prompt governance.

31%
Weight
23
Questions
31
Marks

Solution Design and Model Selection

  • Well-Architected Generative AI Lens
  • Proof of concept with Amazon Bedrock
  • Foundation model selection and benchmarking
  • Model switching with AppConfig and API Gateway
  • Cross-Region inference and graceful degradation
  • Custom model lifecycle with SageMaker AI Model Registry

Data Pipelines for FM Consumption

  • AWS Glue Data Quality and SageMaker Data Wrangler
  • Multimodal data processing
  • Input formatting for Bedrock APIs
  • Entity extraction with Amazon Comprehend

Vector Stores and Retrieval

  • Amazon Bedrock Knowledge Bases
  • Amazon OpenSearch Service and Aurora pgvector
  • Fixed-size and hierarchical chunking
  • Amazon Titan embeddings
  • Hybrid search and reranking
  • Query expansion and decomposition

Prompt Engineering and Governance

  • Amazon Bedrock Prompt Management
  • Bedrock Prompt Flows and prompt chaining
  • Conversation context with DynamoDB
  • Prompt versioning and approval
  • Prompt regression testing

Implementation and Integration

Implementing agentic AI, model deployment, enterprise integration, FM API integration and developer tooling.

26%
Weight
20
Questions
26
Marks

Agentic AI and Tools

  • Strands Agents and AWS Agent Squad
  • Model Context Protocol (MCP) servers on Lambda and ECS
  • ReAct patterns with Step Functions
  • Stopping conditions, timeouts and circuit breakers
  • Human-in-the-loop approvals

Model Deployment

  • On-demand Bedrock vs. provisioned throughput
  • SageMaker AI endpoints
  • Container optimization for GPU and memory
  • Model cascading with smaller models

Enterprise and API Integration

  • API Gateway, EventBridge and webhook integrations
  • Bedrock streaming APIs and WebSockets
  • Asynchronous processing with SQS
  • Exponential backoff and rate limiting
  • CI/CD and generative AI gateways

Application Patterns and Developer Tools

  • AWS Amplify front ends
  • Bedrock Data Automation
  • Amazon Q Developer
  • Troubleshooting with CloudWatch Logs Insights and X-Ray

AI Safety, Security, and Governance

Implementing input and output safety controls, data privacy, governance and responsible AI principles.

20%
Weight
15
Questions
20
Marks

Input and Output Safety

  • Amazon Bedrock Guardrails
  • Prompt injection and jailbreak detection
  • Hallucination reduction with grounding
  • Defense in depth with Comprehend and Lambda validation

Data Security and Privacy

  • VPC endpoints and IAM
  • AWS Lake Formation access control
  • PII detection with Comprehend and Amazon Macie
  • Data masking and retention policies

Governance and Responsible AI

  • SageMaker AI model cards
  • Lineage with the Glue Data Catalog
  • CloudTrail auditing
  • Bias and drift monitoring
  • Bedrock agent tracing for transparency
  • LLM-as-a-judge fairness evaluation

Operational Efficiency and Optimization for GenAI Applications

Optimizing cost, performance and monitoring for generative AI applications.

12%
Weight
9
Questions
12
Marks

Cost Optimization

  • Token estimation and tracking
  • Context window optimization
  • Tiered model selection
  • Prompt caching and semantic caching
  • Batch inference

Performance Optimization

  • Latency-optimized models and streaming
  • Temperature, top-k and top-p tuning
  • Vector index optimization
  • Concurrency management

GenAI Monitoring

  • Bedrock model invocation logging
  • Token usage and cost anomaly tracking
  • Hallucination rate and drift metrics
  • Multi-agent and tool call monitoring

Testing, Validation, and Troubleshooting

Evaluating generative AI systems and troubleshooting content, integration, prompt and retrieval issues.

11%
Weight
8
Questions
11
Marks

Evaluation Systems

  • Amazon Bedrock Model Evaluations
  • RAG evaluation and retrieval relevance scoring
  • Agent evaluation
  • A/B and canary testing
  • Automated quality gates

Troubleshooting GenAI Applications

  • Context window overflow
  • Chunking and embedding quality issues
  • Prompt version comparison
  • X-Ray and CloudWatch diagnosis

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Learning Progress

Foundation Model Integration, Data Management, and Compliance 85%
Implementation and Integration 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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