AWS GenAI Developer Pro study guide
AWS GenAI Developer Pro · Work through your exam, one topic at a time.
The AWS Certified Generative AI Developer – Professional (AIP-C01) is a professional-tier credential for engineers who build generative AI applications on AWS rather than simply calling a foundation model. It suits developers, ML engineers, and solution architects who already know core AWS compute, storage, identity, and networking and now design systems around foundation models. The AWS GenAI Developer Pro study guide has 6 sections and about 1,623 words.
01Foundation Model Integration and Application Development
Candidates must understand how Amazon Bedrock exposes foundation models through a unified InvokeModel and Converse API, how the Converse API normalizes message roles and tool definitions across providers, and how inference parameters ( temperature , topP , maxTokens , stopSequences ) interact. Know the difference between synchronous invocation, streaming responses, and asynchronous batch inference.
296 words, about 1 min02Retrieval-Augmented Generation and Knowledge Integration
Understand the RAG pipeline end to end: ingestion, chunking, embedding, vector storage, retrieval, reranking, and prompt assembly. Know Amazon Bedrock Knowledge Bases: data source connectors (Amazon S3, Confluence, SharePoint, web crawler), the supported vector stores (Amazon OpenSearch Serverless, Aurora PostgreSQL pgvector, Neptune Analytics, Pinecone, Redis Enterprise Cloud), sync jobs, and how chunking strategy (fixed-size, hierarchical, semantic, none) affects recall.
268 words, about 1 min03Model Customization, Fine-Tuning, and Continuous Evaluation
Know the customization spectrum: prompt engineering, few-shot examples, Retrieval-Augmented Generation, then fine-tuning and continued pretraining. Amazon Bedrock supports fine-tuning for select models, with training and validation data in JSONL, and provisioned throughput required to host custom models.
255 words, about 1 min04Security, Identity, and Data Protection for Generative AI
Understand IAM for Bedrock: identity-based policies, resource-based policies, service roles, and least privilege on bedrock:InvokeModel and knowledge base actions. Know AWS PrivateLink and VPC endpoints for private connectivity, AWS KMS customer managed keys for encrypting knowledge base vector stores, and how Bedrock handles data (not used to train base models).
280 words, about 1 min05Operational Excellence, Monitoring, and Cost Optimization
Know observability for generative AI workloads: Amazon CloudWatch metrics for Bedrock (invocations, latency, throttles, token counts), model invocation logging to CloudWatch Logs or S3, and AWS X-Ray for tracing multi-step agent flows. Understand quota management: on-demand throughput quotas, provisioned throughput for predictable load, and cross-region inference profiles to raise effective capacity.
273 words, about 1 min06Responsible AI, Guardrails, and Governance
Understand Amazon Bedrock Guardrails: content filters with configurable strength, denied topics, word and phrase filters, PII filters with redaction, contextual grounding checks for hallucination detection, and automated reasoning checks. Know how guardrails attach to model invocation and to knowledge bases, and that they can be evaluated against test cases before deployment.
251 words, about 1 min