AIP-C01 — AWS Certified Generative AI Developer - Professional
Amazon

AWS Certified Generative AI Developer - Professional (AIP-C01) Practice Questions

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110 questions
June 14, 2026 updated
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Domain coverage

  • Foundation Model Integration, Data Management, and Compliance (31%)
  • Implementation and Integration (26%)
  • AI Safety, Security, and Governance (20%)
  • Operational Efficiency and Optimization for GenAI Applications (12%)
  • Testing, Validation, and Troubleshooting (11%)

Sample Questions (11 of 110 shown)

Q1 Foundation Model Integration, Data Management, and Compliance (31%)
Which Amazon Bedrock feature provides a fully managed Retrieval-Augmented Generation (RAG) workflow that ingests documents from Amazon S3, performs chunking and embedding, and writes vectors to a configurable vector database?
  1. Bedrock Agents
  2. Bedrock Knowledge Bases
  3. Bedrock Studio
  4. Bedrock Model Evaluation
✓ Correct Answer: B
Bedrock Knowledge Bases is the managed RAG service. It connects to Amazon S3, automatically chunks content using the selected embedding model (Titan Embeddings or Cohere Embed), embeds and writes vectors to OpenSearch Serverless, Aurora pgvector, MongoDB Atlas, Pinecone, or Redis Enterprise. At inference time, it retrieves and injects context into the prompt.
Q2 Foundation Model Integration, Data Management, and Compliance (31%)
A team is building a low-latency RAG chatbot expected to handle 5,000 queries per second with vector search latency under 100 ms. They want a fully managed, auto-scaling serverless option. Which Bedrock Knowledge Bases vector store should they choose?
  1. Amazon OpenSearch Serverless (vector engine)
  2. Amazon Aurora PostgreSQL with pgvector
  3. Amazon DocumentDB
  4. Amazon Neptune
✓ Correct Answer: A
OpenSearch Serverless with vector engine is fully managed, auto-scales based on OCU usage, and is Bedrock Knowledge Bases' default high-throughput option. Aurora pgvector is a strong option but requires capacity planning and is provisioned.
Q3 Foundation Model Integration, Data Management, and Compliance (31%)
Which Amazon Bedrock model family is best suited for highly multilingual generation tasks with strong performance across European and Asian languages and is an AWS first-party model?
  1. Anthropic Claude 3.5 Sonnet
  2. Meta Llama 3.1
  3. Amazon Nova Pro
  4. AI21 Jurassic-2
✓ Correct Answer: C
Amazon Nova Pro is an AWS first-party multimodal foundation model with strong multilingual capabilities (200+ languages) and is Bedrock-exclusive. Claude 3.5 Sonnet is excellent at reasoning but owned by Anthropic; Llama 3.1 is Meta's open-source model; Jurassic-2 has been deprecated.
Q4 Foundation Model Integration, Data Management, and Compliance (31%)
A developer wants to switch between Anthropic Claude, Meta Llama, and Amazon Nova using the same application code without rewriting message handling logic. Which API should they use?
  1. InvokeModel
  2. Converse
  3. RetrieveAndGenerate
  4. InvokeAgent
✓ Correct Answer: B
The Bedrock Converse API provides a unified, model-agnostic message interface (system, user, assistant turns) with consistent tool-use semantics, eliminating the model-specific request/response format required by InvokeModel. RetrieveAndGenerate is part of Knowledge Bases, and InvokeAgent is for calling Bedrock Agents.
Q5 Foundation Model Integration, Data Management, and Compliance (31%)
When configuring a Bedrock Knowledge Base, which chunking strategy preserves complete logical units such as paragraphs by using a model to identify natural breakpoints rather than a fixed token count?
  1. Fixed-size chunking
  2. No chunking
  3. Semantic chunking
  4. Hierarchical chunking
✓ Correct Answer: C
Semantic chunking uses an embedding model to detect topic transitions and creates variable-length chunks at natural boundaries, preserving meaning. Fixed-size chunking truncates at token thresholds. Hierarchical chunking creates parent/child chunks at multiple granularities.
Q6 Foundation Model Integration, Data Management, and Compliance (31%)
Which embedding model is AWS's first-party option for Bedrock Knowledge Bases that supports output dimensions of 1024, 512, and 256 for trade-offs between cost and/or quality?
  1. Titan Text Embeddings V2
  2. Cohere Embed English v3
  3. OpenAI text-embedding-3-large
  4. Amazon Comprehend Topic Modeler
✓ Correct Answer: A
Amazon Titan Text Embeddings V2 supports flexible 256/512/1024 dimensions, enabling teams to trade off between vector store cost and retrieval quality. Cohere Embed v3 is also available as a third-party model. OpenAI is not on Bedrock. Comprehend does not produce vector embeddings.
Q7 Foundation Model Integration, Data Management, and Compliance (31%)
A regulated financial services customer must ensure their Bedrock prompts and responses stay entirely within their own AWS account and are not used for AWS service improvement. What guarantee does Bedrock provide by default?
  1. Customer data is automatically used to train AWS proprietary models unless they opt out
  2. Customer prompts and responses are NOT used to train any underlying foundation model and are not shared with model providers
  3. Customer data is shared with model providers for quality monitoring
  4. Customers must purchase Provisioned Throughput to keep data private
✓ Correct Answer: B
Amazon Bedrock contractually guarantees customer data (prompts, completions, embeddings, fine-tuning data) is not used to train any underlying foundation model and is not shared with third-party model providers. This is the default property in all on-demand and provisioned throughput modes.
Q8 Foundation Model Integration, Data Management, and Compliance (31%)
A team has 10,000 unlabeled internal engineering documents and wants to adapt a Bedrock foundation model to better understand its domain vocabulary but has no supervised input/output pairs. Which Bedrock customization technique should they choose?
  1. Fine-tuning
  2. Continued pre-training
  3. RAG with Knowledge Bases
  4. Prompt engineering only
✓ Correct Answer: B
Continued pre-training (domain adaptation) uses unlabeled domain text and updates model weights to learn domain vocabulary and style. Fine-tuning requires labeled JSONL prompt/completion pairs. RAG augments at inference time but does not change weights. Selective Bedrock models support continued pre-training.
Q9 Foundation Model Integration, Data Management, and Compliance (31%)
After fine-tuning a foundation model in Bedrock, what REQUIRED step is needed to invoke the resulting custom model for inference?
  1. No additional action; on-demand inference is automatic
  2. Purchase Provisioned Throughput (model units) for the custom model
  3. Deploy the model to a SageMaker endpoint
  4. Re-import model weights through Bedrock Custom Model Import
✓ Correct Answer: B
Fine-tuned and continued pre-trained models on Bedrock are only served through Provisioned Throughput — model units must be purchased (hourly, 1-month, or 6-month commitment). Custom models do not support on-demand pricing. They run within Bedrock; SageMaker hosting is not used.
Q10 Foundation Model Integration, Data Management, and Compliance (31%)
A team needs to build a Bedrock Knowledge Base based on documents containing confidential personal data subject to GDPR. They require embedding, chunking, and inference to occur only in eu-central-1. Which configuration is correct?
  1. Create the Knowledge Base in us-east-1 because Bedrock RAG is global
  2. Create the Knowledge Base, embedding model, and vector store in eu-central-1, and invoke the model through the eu-central-1 Bedrock endpoint
  3. Bedrock Knowledge Bases automatically locks data residency to the S3 source bucket region
  4. Override the region configuration with a VPC endpoint
✓ Correct Answer: B
Bedrock is regional. For GDPR data residency, the source S3 bucket, Knowledge Base, embedding model calls, and vector store must all be created in the required EU region, and inference must call the Bedrock endpoint in that region. Bedrock is not global.
Q11 Foundation Model Integration, Data Management, and Compliance (31%)
An e-commerce company uses an Amazon Bedrock Agent to handle product returns. The Agent has access to two tools: GetOrderDetails and ProcessRefund. During testing, the Agent fails to handle refunds for orders older than 30 days. The Agent does not call ProcessRefund and instead apologizes to the user. Which troubleshooting step provides the necessary visibility into the Agent's decision logic?
  1. Check CloudWatch Metrics for ProcessRefund Lambda Invocations and Errors
  2. Review CloudTrail Logs for InvokeModel events and check the system prompt
  3. Analyze the Agent Trace with enableTrace=true and check the orchestrationTrace for the rationale step
  4. Use Bedrock Model Evaluation with tool-use accuracy metric
✓ Correct Answer: C
To understand the Agent's specific reasoning for a decision, enabling and checking the Agent Trace provides visibility into the thought process. The orchestrationTrace shows the Agent's rationale and observations from tool returns, offering insight into dynamic reasoning.

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Exam overview

The AWS Certified Generative AI Developer - Professional (AIP-C01) certification is designed for developers who build and deploy production-grade generative AI applications on AWS. This Professional-level exam validates your ability to integrate foundation models (FMs) into business applications using Amazon Bedrock, implement Retrieval-Augmented Generation (RAG) with Knowledge Bases, design agentic AI solutions with Bedrock Agents, and apply AI safety and governance best practices through Guardrails.

Launched in November 2025, the AIP-C01 represents AWS's most advanced generative AI certification to date. The target candidate should have 2+ years of experience building production applications on AWS, general AI/ML or data engineering background, and at least 1 year of hands-on experience implementing GenAI solutions. The exam goes beyond foundational AI knowledge — it tests practical skills in FM selection, prompt engineering, vector store architecture, cost optimization, and Responsible AI implementation using the full Amazon Bedrock ecosystem.

Our AIP-C01 practice test product provides comprehensive preparation with 500+ exam-style questions covering all five official domains. Each question includes detailed explanations that explain why each answer is correct or incorrect, helping you develop the design judgment needed for Professional-level scenarios. The product features domain-wise practice for targeted study, full-length simulation exams (75 questions, 180 minutes) mirroring the real test, and a downloadable PDF study guide for offline review. With questions spanning Foundation Model selection, RAG architecture, Agent implementation, Guardrails configuration, and operational optimization, our materials are designed to accelerate your path to AIP-C01 certification.

Official Exam Domains & Weighting

To successfully pass the AIP-C01 exam, candidates must demonstrate mastery across the following five core domains, with particular emphasis on foundation model integration:
  • Domain 1: Foundation Model Integration, Data Management, and Compliance (31%)
Select and integrate foundation models (Claude, Titan, Cohere, Nova) via Amazon Bedrock, implement RAG with Knowledge Bases for Bedrock and vector stores (OpenSearch Serverless, Aurora pgvector, S3 Vectors), manage data ingestion from S3/DynamoDB/Aurora, apply PII/HIPAA/GDPR compliance controls, and design prompt engineering strategies (few-shot, chain-of-thought, system prompts).
  • Domain 2: Implementation and Integration (26%)
Build agentic AI solutions using Bedrock Agents and AgentCore with Lambda Action Groups, orchestrate workflows with Step Functions and Bedrock Flows, integrate APIs via API Gateway (REST, WebSocket, streaming), and implement Prompt Management with versioned templates and approval workflows.
  • Domain 3: AI Safety, Security, and Governance (20%)
Configure Bedrock Guardrails including content filters, PII redaction, denied topics, prompt attack detection, word filters, and contextual grounding checks; implement Responsible AI practices, IAM policies for Bedrock, VPC endpoints via PrivateLink, KMS encryption, and audit trails with CloudTrail and model invocation logging.
  • Domain 4: Operational Efficiency and Optimization for GenAI Applications (12%)
Optimize GenAI costs through Provisioned Throughput vs. On-Demand tradeoffs, manage token consumption and context compression, implement caching strategies, select optimal models for latency/cost balance, and design multi-region deployment for availability and throughput.
  • Domain 5: Testing, Validation, and Troubleshooting (11%)
Evaluate model quality using Bedrock Model Evaluation and LLM-as-a-Judge, detect and mitigate hallucinations, conduct A/B testing and prompt iteration, implement observability with CloudWatch and X-Ray, and troubleshoot response quality, latency, and error patterns.

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Frequently Asked Questions

The key difference is depth and scope. AIF-C01 is a Foundational-level exam covering broad AI/ML concepts, ideal for business analysts and managers. AIP-C01 is a Professional-level exam requiring hands-on development skills — you'll need to design and implement GenAI solutions using Amazon Bedrock, including RAG architectures, agentic AI, prompt engineering, and Guardrails. Think of AIF-C01 as knowing what GenAI can do, and AIP-C01 as knowing how to build it. AWS recommends completing AIF-C01 before attempting AIP-C01.

The exam heavily focuses on five key Bedrock capabilities: (1) Knowledge Bases for Bedrock — RAG implementation, chunking strategies, vector store selection, and grounding; (2) Bedrock Agents & AgentCore — multi-agent collaboration, Lambda Action Groups, and memory management; (3) Bedrock Guardrails — content filters, PII redaction, denied topics, prompt attack protection, and contextual grounding checks; (4) Bedrock Flows — workflow orchestration and Prompt Management; (5) Model Evaluation — LLM-as-a-Judge, hallucination detection, and automated/human evaluation. Understanding the tradeoffs between these features across different use cases is critical.

AWS recommends 2+ years of production application development on AWS, 1+ year of GenAI implementation experience, and familiarity with AIF-C01-level AI/ML concepts. You should be comfortable with AWS SDKs (Python, Java, or TypeScript), infrastructure as code (CloudFormation, CDK), IAM security best practices, and core AWS services (Lambda, API Gateway, Step Functions, S3). Experience with Amazon Bedrock console operations and API usage is highly beneficial. Our practice tests help bridge knowledge gaps regardless of your starting point.

Our product is purpose-built for the AIP-C01's Bedrock-centric exam blueprint. It includes 500+ questions organized by domain with detailed explanations covering FM selection tradeoffs, RAG architecture decisions (when to use OpenSearch Serverless vs. Aurora pgvector vs. S3 Vectors), Agent configuration patterns, Guardrails policy design, and cost optimization strategies. All questions replicate the exam's multiple choice and multiple response formats. You also get full-length 180-minute simulation exams, domain-wise practice modules, and a downloadable PDF study guide featuring key Bedrock service comparisons and architecture decision trees.

Required study time varies by background: AWS beginners typically need 120–160 hours (3–4 months); AIF-C01 holders require 60–90 hours (1.5–2.5 months); those with GenAI hands-on experience need 40–60 hours (~1 month); and experienced Bedrock developers can prepare in 20–35 hours (2–3 weeks). We recommend a structured approach: start with AWS Skill Builder's official AIP-C01 Exam Prep, then practice with hands-on Bedrock labs, followed by intensive practice tests, and finish with full-length simulation exams.

Yes — RAG and vector store architecture is one of the most heavily tested topics within Domain 1 (31% weight). You need to understand how to implement Knowledge Bases for Bedrock with various vector store options: Amazon OpenSearch Serverless (for full-text + vector search), Aurora pgvector (for transactional + vector workloads), S3 Vectors (for simplified serverless RAG), and Neptune Graph RAG (for relationship-rich knowledge domains). Questions test your ability to select the right vector store for specific use cases, configure chunking strategies, and implement proper grounding techniques.

The AIP-C01 is one of AWS's newest and most sought-after Professional certifications, directly aligned with the exploding demand for GenAI development skills. Certified professionals are well-positioned for roles such as Generative AI Developer, AI Solutions Architect, ML Engineer (GenAI focus), and Cloud AI Specialist. As enterprises move GenAI projects from proof-of-concept to production, AIP-C01 holders demonstrate the exact skills needed to lead these initiatives. Combined with MLA-C01 (Machine Learning Engineer Associate), you become a highly competitive AI/ML professional with both ML engineering and GenAI development expertise.