AWS AIP-C01 Practice Test: Generative AI Developer Professional

Last revised: July 25, 2026

Practice AWS Certified Generative AI Developer - Professional (AWS AIP-C01) in IT Mastery with focused sample pages, topic drills, timed mock exams, detailed explanations, and the current question bank.

Use IT Mastery for interactive practice with timed mocks, topic drills, progress tracking, and detailed explanations across web and mobile. Focused topic pages and the static diagnostic page preview how this exam handles Amazon Bedrock, RAG, agents, safety, governance, optimization, and production troubleshooting.

AIP-C01 validates advanced technical expertise in building and deploying production-ready generative AI solutions on AWS, especially solutions that integrate foundation models into applications and business workflows.

Practice preview and focused pages

Use this page to start the web app and choose the right public preview before longer mixed practice. For sample exam questions, use the focused topic pages, quick review, and free-practice page in this exam section; the interactive app remains the primary practice path.

  • Focused topic pages: drill focused topics including AI Safety, Security, and Governance; FM Integration and Data; and other domains with explanations.
  • Quick review: Bedrock, RAG, agents; practice-ready checks.
  • Free practice exam: Try 75 free AWS Certified Generative AI Developer - Professional (AWS AIP-C01) questions across the exam domains, with explanations, then continue with IT Mastery practice.

What this AIP-C01 practice page gives you

  • a direct web entry for AIP-C01 practice in IT Mastery
  • focused topic pages and free-practice coverage for previewing question style
  • topic drills and mixed sets across Bedrock, RAG, agents, safety, governance, optimization, and troubleshooting
  • a clear web preview path for previewing question style before deeper practice
  • the same IT Mastery account across web and mobile

Who AIP-C01 is for

  • developers building production-grade generative AI applications on AWS
  • candidates with AWS application experience who need deeper Amazon Bedrock, RAG, agent, governance, monitoring, and optimization judgment
  • teams moving from AI proofs of concept into secure, observable, cost-aware production GenAI systems

AIP-C01 exam snapshot

  • Vendor: AWS
  • Official exam name: AWS Certified Generative AI Developer - Professional
  • Exam code: AIP-C01
  • Category: Professional
  • Items: 75 total, including 65 scored and 10 unscored
  • Exam time: 180 minutes
  • Question types: multiple-choice and multiple-response
  • Passing score: 750 scaled
  • Current IT Mastery status: live practice available

AIP-C01 questions reward production-grade GenAI decisions: choosing the right foundation model integration pattern, grounding and evaluating outputs, securing data and identities, controlling cost and latency, and troubleshooting deployed AI workflows.

Topic coverage for AIP-C01

DomainWeight
Foundation Model Integration, Data Management, and Compliance31%
Implementation and Integration26%
AI Safety, Security, and Governance20%
Operational Efficiency and Optimization for GenAI Applications12%
Testing, Validation, and Troubleshooting11%

AIP-C01 production GenAI decision filters

Use these filters before choosing between two advanced Bedrock or GenAI architecture options:

  • Grounding strategy: decide whether the workload needs prompt engineering, RAG, knowledge bases, fine-tuning/customization, agents, or a simpler managed AI service.
  • Data and compliance boundary: protect customer data, training data, vector stores, logs, and model inputs with the right IAM, encryption, retention, and residency controls.
  • Safety and governance: apply guardrails, content filters, human review, evaluation, auditability, and responsible-AI controls where output risk is material.
  • Operational efficiency: balance latency, throughput, token cost, model choice, caching, batching, prompt size, and endpoint or service limits.
  • Troubleshooting signal: separate retrieval failure, prompt failure, model limitation, permissions failure, evaluation weakness, and downstream integration defects.

AIP-C01 readiness map

AreaWhat strong readiness looks like
FM integration, data, and complianceYou can design grounded GenAI applications that respect data boundaries, compliance expectations, and model-access constraints.
Implementation and integrationYou can connect Bedrock, agents, tools, APIs, vector stores, prompt workflows, and application code into production workflows.
Safety, security, and governanceYou can apply guardrails, IAM, encryption, monitoring, human oversight, and responsible-AI controls to risky outputs.
Operational efficiencyYou can optimize latency, cost, throughput, model selection, token usage, and scaling without weakening quality or safety.
Testing and troubleshootingYou can diagnose bad retrieval, poor prompts, unsafe outputs, model mismatch, missing permissions, and weak evaluation design.

How to use the AIP-C01 simulator efficiently

  1. Start with foundation-model integration, data-management, and compliance drills so you can separate architecture fit from implementation detail.
  2. Review every miss until you can explain why the best answer handles grounding, access, safety, and operational control better than the distractors.
  3. Move into mixed sets once you can switch between RAG, knowledge bases, agents, prompt workflows, evaluation, and observability without losing the scenario’s priority.
  4. Finish with timed runs so the 180-minute professional-level pace feels normal before test day.

Final 7-day AIP-C01 practice sequence

DayPractice focus
7Open the web app for a timed mixed set, then use the public diagnostic page if you need to separate misses into architecture, implementation, governance, optimization, and troubleshooting.
6Drill foundation-model integration, RAG, vector search, knowledge bases, data handling, and compliance boundaries.
5Drill agents, tool use, prompt workflows, API integration, orchestration, and application implementation scenarios.
4Drill safety, guardrails, responsible AI, IAM, encryption, monitoring, and audit controls.
3Drill cost, latency, token usage, model selection, scaling, and operational optimization decisions.
2Complete a timed mixed set and explain the production failure mode or trade-off behind each miss.
1Review only weak GenAI patterns and troubleshooting signals; avoid late memorization of unfamiliar feature details.

When AIP-C01 practice is enough

If you can score above roughly 75% on several unseen mixed attempts and explain how each answer handles grounding, safety, operations, and cost, you are ready to treat the exam as a reasoning test rather than a memorization exercise. More practice should improve production GenAI judgment, not just recognition of repeated scenarios.

Free study resources

Use this IT Mastery page for live practice, topic drills, timed mocks, explanations, and app access.

Web preview and premium practice

  • Web/public preview: a smaller web set so you can validate the question style and explanation depth.
  • Premium: interactive web-app practice with focused drills, mixed sets, timed mock exams, detailed explanations, and progress tracking across web and mobile.

Good next pages after AIP-C01

  • AIF-C01 if you need AWS AI and GenAI fundamentals before the professional route
  • MLA-C01 if you also need machine learning lifecycle and deployment practice
  • DEA-C01 if your GenAI systems depend on governed data pipelines
  • AWS certification hub if you are still comparing AWS routes

Official sources

AIP-C01 generative AI delivery map

Use this map to connect individual items to the AWS Generative AI Developer Professional decisions this practice page tests.

    flowchart LR
	  S1["Business GenAI requirement"] --> S2
	  S2["Choose Bedrock model and retrieval pattern"] --> S3
	  S3["Design prompt guardrail and data controls"] --> S4
	  S4["Integrate through APIs workflows and agents"] --> S5
	  S5["Evaluate safety quality and latency"] --> S6
	  S6["Operate cost monitoring and rollback"]

Mini Glossary

  • Bedrock Guardrails: AWS controls for filtering or constraining model inputs and outputs.
  • Knowledge Base: Bedrock capability for retrieval-grounded generation over indexed data.
  • Provisioned Throughput: Reserved model capacity for predictable Bedrock inference.
  • RAG: Retrieval augmented generation using retrieved context to ground model responses.
  • Token: Text unit processed by a foundation model and often used for cost or limit calculation.

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