AWS Certified Generative AI Developer Professional (AIP‑C01) Practice Exams
About the AWS AIP-C01 exam
Exam at a glance
AWS's developer-focused Generative AI certification at the professional tier.
Who AIP-C01 is for
AIP-C01 targets developers building generative AI applications on AWS — primarily on Amazon Bedrock and Amazon SageMaker. It is a different exam from AIF-C01 (Foundational tier, broad AI concepts at an awareness level) and from MLA-C01 (Associate tier, general ML engineering across the lifecycle). AIP-C01 is specifically about Gen AI development — foundation models, RAG, agents, prompt engineering, guardrails, and shipping LLM-powered products to production.
- Software engineers building LLM-powered apps on AWS.
- ML engineers shifting from traditional ML to Gen AI workloads.
- Solutions architects designing enterprise Gen AI systems.
Core services tested
- Amazon Bedrock — model catalog, InvokeModel and Converse APIs, Agents, Knowledge Bases (managed RAG), Guardrails, on-demand vs provisioned throughput.
- Amazon SageMaker — JumpStart foundation models, real-time and async inference endpoints, fine-tuning workflows.
- Vector stores — Amazon OpenSearch Serverless, Aurora PostgreSQL with pgvector, Bedrock Knowledge Bases as a managed vector layer.
- Orchestration — Lambda + Step Functions for agent loops; API Gateway for client-facing endpoints; DynamoDB for session state and conversation history.
- Data + storage — S3 ingestion for Knowledge Bases, KMS for encryption, IAM for least-privilege agent roles.
- Observability — CloudWatch metrics for token usage and invocation latency, model evaluation jobs, prompt logging.
Prerequisites
AWS recommends 2+ years of production AWS experience plus 1+ year of hands-on generative AI implementation. No formal prereqs — you can skip AIF-C01 and Associate certifications if you already have the experience, but a strong developer background (CLF-C02 + DVA-C02 level) is assumed.
Why take this certification
- First AWS Professional-tier Gen AI credential. AIP-C01 is the highest-tier AWS certification dedicated to generative AI development, signaling production-grade competence rather than general awareness.
- Hiring signal in a hot market. Gen AI engineering roles are among the fastest-growing in the AWS partner ecosystem in 2026, and AIP-C01 directly maps to the skills employers ask for: Bedrock, RAG, agents, evaluation.
- Differentiates from broader ML credentials. Where MLA-C01 covers the full ML lifecycle and AIF-C01 covers concepts, AIP-C01 is the only AWS exam that specifically validates production Gen AI application development.
- Forces hands-on Bedrock fluency. Unlike awareness-level certs, AIP-C01 is scenario-heavy — you can't pass without having actually built RAG pipelines, agent workflows, and guardrail policies on AWS.
What you'll learn in the AIP-C01 exam
AIP-C01 validates that you can design, build, and operate production generative AI applications on AWS. Most questions describe a Gen AI workload — a chatbot, document Q&A system, code assistant, agentic workflow — with constraints (latency, cost, accuracy, compliance) and ask you to choose the architecture and AWS service combination that fits.
Amazon Bedrock — the exam's center of gravity
- Model catalog and selection: choosing among Anthropic Claude, Meta Llama, Mistral, Amazon Nova, Cohere, Stability AI — by latency, context window, modality, and cost.
- Model invocation: InvokeModel vs Converse API, streaming vs non-streaming, batch inference, cross-region inference profiles.
- Bedrock Agents: action groups, function calling, multi-step reasoning, agent traces, OpenAPI schemas for tool integration.
- Bedrock Knowledge Bases: managed RAG with S3 ingestion, chunking strategies, embedding model choice, hybrid search, retrieval relevance tuning.
- Bedrock Guardrails: denied topics, content filters, sensitive-information masking, contextual grounding checks.
- Pricing models: on-demand vs Provisioned Throughput, model units, cost-per-token economics, batch discounts.
RAG patterns
- Chunking strategies (fixed-size, semantic, parent-document), embedding model selection (Titan, Cohere Embed), and chunk-overlap trade-offs.
- Vector store choice — Amazon OpenSearch Serverless vs Aurora pgvector vs Bedrock-managed Knowledge Bases — by scale, latency, and cost.
- Hybrid retrieval (vector + keyword), re-ranking with cross-encoders, and metadata filtering.
- Grounding answers, citation strategies, and detecting hallucinations.
Foundation-model fine-tuning
- When to fine-tune vs prompt-engineer vs use RAG — the decision framework AWS expects you to articulate.
- Fine-tuning on Bedrock and on SageMaker JumpStart, instruction tuning vs continued pre-training.
- Custom model imports, model evaluation jobs, and continuous evaluation pipelines.
Prompt engineering, evaluation, and responsible AI
- Prompt techniques — zero-shot, few-shot, chain-of-thought, ReAct, structured-output prompting (JSON mode, tool use).
- Evaluation — automated metrics, model-graded evaluations, human-in-the-loop with Amazon SageMaker Ground Truth.
- Responsible AI — Bedrock Guardrails policies, PII detection and redaction, bias evaluation, content moderation pipelines.
Production integration patterns
- Lambda + Bedrock for serverless inference endpoints; API Gateway in front for auth + throttling.
- DynamoDB for conversation history and per-user session state, with TTL for ephemeral context.
- OpenSearch Serverless or Aurora pgvector as the vector backing store; Step Functions orchestrating multi-step agent workflows.
- Observability — CloudWatch metrics on token usage and latency, prompt + response logging with PII scrubbing, cost attribution per tenant.
How the practice exams help
Each free question and every premium exam mirrors the scenario format AWS uses — a real Gen AI architecture problem, multiple plausible solutions, and one or two that actually fit the stated constraints. Explanations cover the trade-offs (latency vs cost vs accuracy, managed vs self-hosted vector stores, fine-tune vs RAG) so you learn to reason about Gen AI architecture, not memorize service trivia.
How to prepare for the AIP-C01 exam
AIP-C01 is a Professional-tier exam — preparation is heavier than for any Associate cert. Plan 8–12 weeks of focused study combined with real hands-on time in Amazon Bedrock and SageMaker. Recommended approach:
- Anchor the blueprint (1 week). Review the official AWS Skill Builder AIP-C01 learning path and download the exam guide PDF. Map every objective to the AWS services that own it — most map to Amazon Bedrock, with secondary coverage of SageMaker, OpenSearch, and Lambda/Step Functions.
- Hands-on Bedrock (3–4 weeks). Build at least three projects in your AWS account using Free Tier credits where available: (1) a Bedrock Knowledge Base on S3 documents with both OpenSearch Serverless and pgvector backends, (2) a Bedrock Agent with at least two action groups invoking Lambda functions, (3) a guardrail policy with denied topics, content filters, and contextual grounding. Hands-on is non-negotiable — AIP-C01 scenarios assume you've actually wired these services together.
- SageMaker foundation models + fine-tuning (1–2 weeks). Deploy a foundation model via SageMaker JumpStart. Run a small instruction-tuning job (Bedrock or SageMaker) and a model evaluation job. Understand when to fine-tune vs use RAG vs prompt-engineer — this decision framework appears repeatedly on the exam.
- Whitepapers + responsible AI (1 week). Read the AWS responsible AI documentation, the Bedrock Guardrails best-practices guide, and the AWS Well-Architected Generative AI Lens. AWS expects you to articulate evaluation strategies, bias detection, and PII handling — these are explicitly tested.
- Practice exams (1–2 weeks). Take timed practice tests to identify weak areas. Detailed explanations on every option help you learn the reasoning behind RAG vs fine-tuning trade-offs, vector-store choice, and agent vs single-prompt patterns. Aim for consistent 80%+ scores before scheduling.
Recommended timeline
8–12 weeks of focused study (10–15 hours per week). Developers with strong existing Bedrock experience may compress to 6–8 weeks; newcomers to Gen AI on AWS should plan for the full 12 weeks plus an extra month of hands-on before opening the exam guide.
Background you should already have
AWS assumes 2+ years of production AWS experience and 1+ year of hands-on Gen AI work. If you're missing the AWS basics, sit CLF-C02 Cloud Practitioner or build through DVA-C02 Developer Associate first. If you're new to AI concepts in general, AIF-C01 AI Practitioner is a softer on-ramp before AIP-C01.
Official resources
Use the AWS Skill Builder AIP-C01 learning path and the official AWS exam guide PDF. AWS's free training portal hosts both. Pair them with the Amazon Bedrock developer documentation and the Generative AI Lens of the AWS Well-Architected Framework.
Frequently asked questions
Are these actual exam questions?
No. All Nex Arc AWS AIP-C01 practice questions are original content based on the provider's published exam blueprint, official documentation, and real-world scenarios across every topic the exam covers. We do not offer brain dumps, leaked questions, or actual exam content. Using or distributing real exam questions violates certification provider agreements and can result in certification revocation.
How many questions are in the AWS AIP-C01 exam?
The AWS AIP-C01 exam consists of 75 questions that you need to complete in 180 minutes. Questions are either multiple choice (one correct answer) or multiple response (two or more correct answers).
What's the AIP-C01 passing score?
The passing score is 750 out of 1000. AWS uses a scaled scoring model, and not all questions carry the same weight. Focus on understanding generative AI architectures and trade-offs rather than memorizing service names.
How do I access the full AIP-C01 practice exam?
Click on the Buy Now button in the sidebar to purchase the complete AWS AIP-C01 course. After payment, you'll have instant access to 12 practice exams with hundreds of questions covering Amazon Bedrock, RAG, agents, fine-tuning, guardrails, and production GenAI patterns — detailed explanations and lifetime access.
What are the prerequisites for the AIP-C01 exam?
AWS recommends two or more years of experience building production applications on AWS plus at least one year of hands-on generative AI implementation experience. There are no formal prerequisites — you do not need AIF-C01 or any Associate certification first. Familiarity with Amazon Bedrock, SageMaker, prompt engineering, retrieval-augmented generation (RAG), and Python or TypeScript LLM SDKs is essential before attempting this exam.
How long is the AIP-C01 certification valid?
AWS certifications are valid for three years from the date you pass the exam. To recertify AIP-C01, you can either retake the current version of the exam or pass a higher-tier AWS certification before your current certification expires. A 50% discount voucher for the recertification exam is available through your AWS Certification Account. AWS does NOT offer Skill Builder credits or a continuing-education recertification path — exam-based recertification is the only option.
What's the exam cost and can I retake it if I fail?
The AIP-C01 exam costs $300 USD globally (Professional tier pricing, converted to local currency). The exam is delivered through Pearson VUE at testing centers or via online proctoring. If you fail, you must wait 14 days before retaking the exam at full price. There is no limit to the number of attempts, but each attempt requires a new $300 payment. AWS occasionally offers 50% discount vouchers for recertification or through AWS Partner Network programs. Practice thoroughly before attempting to minimize retake costs.
Which AWS services are most heavily tested on AIP-C01?
Amazon Bedrock dominates the exam — model catalog selection, InvokeModel and Converse APIs, Bedrock Agents, Bedrock Knowledge Bases (managed RAG), Bedrock Guardrails, and provisioned-throughput vs on-demand pricing. Other heavily tested services include Amazon SageMaker (foundation-model hosting, fine-tuning, JumpStart), Amazon OpenSearch Serverless and Aurora pgvector (vector stores for RAG), AWS Lambda + Step Functions (orchestration), Amazon API Gateway, Amazon DynamoDB (session state), Amazon S3 (data ingestion for Knowledge Bases), AWS KMS, IAM (least-privilege roles for agents), and CloudWatch (token-usage metrics, model invocation latency).
How does AIP-C01 compare to AIF-C01 and MLA-C01?
AIF-C01 (AI Practitioner) is a Foundational-tier exam covering general AI and ML concepts at an awareness level — no hands-on requirement. MLA-C01 (Machine Learning Engineer Associate) is Associate tier and covers the full traditional ML lifecycle on AWS — data prep, training, deployment, MLOps — not specifically generative AI. AIP-C01 is Professional tier and is exclusively about building production generative AI applications: Amazon Bedrock, RAG architectures, agentic workflows, fine-tuning foundation models, prompt engineering at scale, evaluation, guardrails, and responsible AI. If you're new to AI on AWS, start with AIF-C01. If you build traditional ML pipelines, target MLA-C01. AIP-C01 is for developers shipping LLM-powered products.
Can I retake the AIP-C01 exam if I fail?
Yes. AWS allows retakes after a 14-day waiting period. You pay the full $300 exam fee again. There is no limit on total attempts.