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AWS Certified Generative AI Developer - Professional (AIP-C01) practice exam and study path

A free, timed 75-question AIP-C01 practice exam — 180 minutes, the same length as the real exam, scored by domain, with every option explained after you submit. Also 175 practice questions you can check one at a time, a page for every task in the exam guide, and a study path. No sign-up.

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Generative AI Developer - Professional exam pack:

$39once

Building production generative AI: integrating foundation models, retrieval and agents, safety and governance, cost and performance, and testing.

The exam

Exam code
AIP-C01
Questions
75 questions (65 scored)
Time
180 minutes
Passing score
750 of 1,000 (scaled)
Exam fee
$300 (USD)
Question types
multiple choice, multiple response

From AWS's certification page and exam guide, checked Oct 10, 2026. The exam is scored as a whole (compensatory): there is no pass mark per domain.

Domains and weights

  • Domain 1: Foundation Model Integration, Data Management, and Compliance

    31%

  • Domain 2: Implementation and Integration

    26%

  • Domain 3: AI Safety, Security, and Governance

    20%

  • Domain 4: Operational Efficiency and Optimization for GenAI Applications

    12%

  • Domain 5: Testing, Validation, and Troubleshooting

    11%

On our roadmap this comes after Machine Learning Engineer - Associate or Developer - Associate. See the whole roadmap.

Practice

175 original practice questions, every option explained with the AWS documentation page that proves it. Free, no sign-up; progress stays on this device.

Your results on this device →

More practice exams

The lessons, the sample questions on every task page and the first full practice exam are free and stay free. A pack adds the further timed AIP-C01 practice exams.

Generative AI Developer - Professional exam pack

$39once

  • Every paid AIP-C01 practice exam, timed, with every option explained
  • The next version of this exam included when it changes
  • Lifetime access — one payment, no subscription
  • 14-day refund, no conditions
  • 14-day refund, no conditions: ask within 14 days of buying and you get the whole payment back.
  • No pass guarantee. These are practice exams written by us from the exam guide and the AWS documentation; a score here does not predict your result on the real exam.

Study path

One module per exam domain. Lessons that already exist on BytePatterns are linked; the rest are being written.

  1. Module 1: Foundation Model Integration, Data Management, and Compliance

    1. 1Prompting, retrieval, agents or customization: choosing the architecture for a use caseTask 1.1

      Partly covered by: What Is an LLM, Retrieval-Augmented Generation, Fine-Tuning vs Prompting

    2. 2Proofs of concept on Amazon Bedrock and design reviews with the Well-Architected Generative AI LensTask 1.1

      Partly covered by: Design a System on AWS

    3. 3Choosing a foundation model: capability, context window, cost, Regional availability and lifecycleTask 1.2

      Partly covered by: Context Windows, What Is an LLM

    4. 4Switching models without code changes and staying up: Converse, AppConfig, cross-Region inference and circuit breakersTask 1.2

      Partly covered by: Rate Limiting

    5. 5Customized models: fine-tuning, distillation, LoRA adapters, Model Registry and safe rolloutsTask 1.2

      Partly covered by: Fine-Tuning vs Prompting, Adapters and LoRA

    6. 6Data quality and processing for model input: Glue Data Quality, Transcribe, Data Automation and request formatsTask 1.3

      Partly covered by: Tokenization

    7. 7Vector stores on AWS: managed knowledge bases, OpenSearch, Aurora pgvector and S3 VectorsTask 1.4

      Partly covered by: Vector Databases, Approximate Neighbours, Embeddings

    8. 8Keeping an index current: metadata, incremental sync and direct ingestionTask 1.4

      Partly covered by: Lambda & Event-Driven Design

    9. 9Chunking, embeddings, hybrid search, reranking and query decompositionTask 1.5

      Partly covered by: Chunking and Reranking, Embeddings, Cosine Similarity

    10. 10Exposing retrieval as a tool: function calling and MCP through AgentCore GatewayTask 1.5

      Partly covered by: The Tool-Use Loop

    11. 11Prompt management, versions, structured outputs and prompt flowsTask 1.6

      Partly covered by: Temperature and Sampling

  2. Module 2: Implementation and Integration

    1. 12Agents on AgentCore: Runtime sessions, Memory, Gateway tools, Identity and PolicyTask 2.1

      Partly covered by: Agents and Tools, The Tool-Use Loop

    2. 13Keeping agents in bounds: iteration limits, timeouts, human approval and multi-agent coordinationTask 2.1

      Partly covered by: Agents and Tools

    3. 14Deploying models: on-demand, service tiers, Provisioned Throughput, SageMaker AI endpoints and inference componentsTask 2.2

      Partly covered by: Training vs Inference, Quantization, The KV Cache

    4. 15Event-driven and API integration of foundation models into existing systemsTask 2.3

      Partly covered by: SQS vs SNS vs EventBridge, Message Queues

    5. 16Identity, least privilege, data residency and governed GenAI gateways across accountsTask 2.3

      Partly covered by: Shared Responsibility & IAM, VPC: Subnets, NAT & Firewalls

    6. 17Calling models reliably: streaming, asynchronous processing, retries, rate limits and fallbacksTask 2.4

      Partly covered by: WebSockets and Realtime, Rate Limiting, Model Routing and Fallbacks

    7. 18Routing requests to the right modelTask 2.4

      Lesson: Model Routing and Fallbacks

    8. 19API design for GenAI, document processing and prompt chaining in applicationsTask 2.5

      Partly covered by: Designing a REST API, Lambda & Event-Driven Design

    9. 20Developer tooling and troubleshooting aids for GenAI applicationsTask 2.5

      Partly covered by: CloudWatch, Alarms & X-Ray, Tracing a Request

  3. Module 3: AI Safety, Security, and Governance

    1. 21Amazon Bedrock Guardrails: content filters, prompt attacks, denied topics, grounding and automated reasoningTask 3.1

      Partly covered by: Guardrails

    2. 22Defense in depth for model input and output, including agents and toolsTask 3.1

      Partly covered by: Guardrails, The Tool-Use Loop

    3. 23Private connectivity, least privilege, encryption and retention for GenAI dataTask 3.2

      Partly covered by: VPC: Subnets, NAT & Firewalls, Shared Responsibility & IAM, S3: Consistency, Classes, Lifecycle

    4. 24Finding and masking PII: Comprehend, Macie, guardrail filters and log data protectionTask 3.2

      Lesson: Finding and Masking PII

    5. 25Governance and audit: model cards, lineage, CloudTrail, invocation logs and organization-wide guardrailsTask 3.3

      Partly covered by: CloudWatch, Alarms & X-Ray

    6. 26Responsible AI in practice: source attribution, fairness evaluation and documented limitsTask 3.4

      Partly covered by: Evaluating LLMs, LLM as a Judge

  4. Module 4: Operational Efficiency and Optimization for GenAI Applications

    1. 27Token economics: max_tokens, prompt caching, batch inference, service tiers and model tieringTask 4.1

      Partly covered by: Tokenization, Context Windows, AWS Cost Levers, Model Routing and Fallbacks

    2. 28Semantic caching and cost attribution per applicationTask 4.1

      Partly covered by: Caching, Invalidation and Eviction

    3. 29Latency and throughput for GenAI: streaming, parallel calls, retrieval tuning and capacity planningTask 4.2

      Partly covered by: The KV Cache, Speculative Decoding, Vertical vs Horizontal Scaling

    4. 30Inference parameters and A/B tests for output qualityTask 4.2

      Partly covered by: Temperature and Sampling

    5. 31Monitoring GenAI: Bedrock metrics, invocation logs, CloudWatch generative AI observability and agent tracingTask 4.3

      Partly covered by: CloudWatch, Alarms & X-Ray, Observability Basics, Tracing a Request

  5. Module 5: Testing, Validation, and Troubleshooting

    1. 32Evaluating models and RAG: automatic metrics, LLM-as-a-judge, human review and RAG evaluationsTask 5.1

      Partly covered by: Evaluating LLMs, LLM as a Judge

    2. 33Evaluating agents and gating releases: AgentCore Evaluations, regression tests and deployment validationTask 5.1

      Partly covered by: Evaluating LLMs

    3. 34Troubleshooting context, API, prompt and retrieval problemsTask 5.2

      Partly covered by: Context Windows, Chunking and Reranking, CloudWatch, Alarms & X-Ray

Every task in the exam guide

The exam guide splits each domain into task statements. Each one has a page with a short summary, the lessons that teach it and open sample questions.

Domain 1: Foundation Model Integration, Data Management, and Compliance

Domain 2: Implementation and Integration

Domain 3: AI Safety, Security, and Governance

Domain 4: Operational Efficiency and Optimization for GenAI Applications

Domain 5: Testing, Validation, and Troubleshooting

How these questions are made

  • Written by us from the exam guide's task statements and the AWS documentation — never from real exam content.
  • Every option carries its own explanation and a link to the AWS page that proves it.
  • A question enters a timed exam only after an independent check; until then it is a practice question.
  • Results are a plain percentage, per domain too — not an imitation of AWS's scaled score.

Questions about the AIP-C01 practice exam

Is the AIP-C01 practice exam free?
Yes. The full practice exam (75 questions, 180 minutes) is free with no sign-up, and so are the 175 practice questions, the task pages and the study path. Your attempts are saved in this browser, on this device.
How many questions does it have, and is it timed?
75 questions in 180 minutes: the same number of questions and the same time as the real AIP-C01 exam. When the time runs out, the exam is submitted as it stands, and unanswered questions count as wrong.
Are the answers explained?
After you submit, not during: like the real exam, the timed exam gives no feedback until the end. Then you get your score overall and per exam domain, and every question with each option explained and a link to the AWS documentation page behind it. In practice mode an answer is explained as soon as you check it.
What does the Generative AI Developer - Professional exam pack add?
3 more timed practice exams (Practice exam 1: 75 questions, 180 minutes; Practice exam 2: 75 questions, 180 minutes; Practice exam 3: 75 questions, 180 minutes), each scored and explained the same way, and the next version of this exam when it changes. One payment, lifetime access, no subscription. Everything above stays free. See the pack.
Can I get a refund?
Yes, with no conditions: ask within 14 days of buying and you get the whole payment back. The details are in the terms.
Is this an official AWS practice exam?
No. BytePatterns is not affiliated with, endorsed or sponsored by Amazon Web Services. Every question is written by us from the public exam guide and the AWS documentation, never from real exam content, and a score here does not predict your result on the real exam. AWS lists its own preparation resources on its certification page.