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Course and practice questions

Generative AI Developer course

Generative AI Developer – Professional (AIP-C01): 5 modules, 15 lessons and 5 quizzes. This exam is multiple choice, so the practice is the course and original questions. The lab runtime is kubeadm clusters only, and this page does not include an AWS account.

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What's included

A course and practice questions

5 modules, 15 lessons and 5 quizzes (40 original questions). About 2 h 37 min of reading. The lab runtime is kubeadm clusters only, so this page does not include an AWS account.

  • 5 modules

    15 lessons mapped to the domains in the public exam guide.

  • 5 quizzes

    40 original practice questions, with an explanation for every answer. They are not exam items.

  • 180-minute exam

    The length published for this exam. This page does not offer a timed mock.

Exam domains

Domains from the public guide

The domain weights below sum to 100. The official page is the source of truth: Generative AI Developer – Professional exam page.

  • Foundation Model Integration, Data Management, and Compliance

    31%of the exam

    6 competencies

    • Analyze requirements and design GenAI solutions
    • Select and configure foundation models
    • Implement data validation and processing pipelines for foundation model consumption
    • Design and implement vector store solutions
    • Design retrieval mechanisms for foundation model augmentation
    • Implement prompt engineering strategies and governance for foundation model interactions

  • Implementation and Integration

    26%of the exam

    5 competencies

    • Implement agentic AI solutions and tool integrations
    • Implement model deployment strategies
    • Design and implement enterprise integration architectures
    • Implement foundation model API integrations
    • Implement application integration patterns and development tools

  • AI Safety, Security, and Governance

    20%of the exam

    4 competencies

    • Implement input and output safety controls
    • Implement data security and privacy controls
    • Implement AI governance and compliance mechanisms
    • Implement responsible AI principles

  • Operational Efficiency and Optimization for GenAI Applications

    12%of the exam

    3 competencies

    • Implement cost optimization and resource efficiency strategies
    • Optimize application performance
    • Implement monitoring systems for GenAI applications

  • Testing, Validation, and Troubleshooting

    11%of the exam

    2 competencies

    • Implement evaluation systems for GenAI
    • Troubleshoot GenAI applications

Curriculum

5 modules, 15 lessons, 5 quizzes

Lesson titles from the course. The questions stay in the quizzes, and the answers are not published here.

01Foundation models, data, and prompts3 lessons · 34 min · quiz, 8 questions

Decide when a foundation model is the right shape, prepare what it will read, and govern the prompt that calls it. Domain: Foundation Model Integration, Data Management, and Compliance.

  1. lessonRequirements and model choice12 min
  2. lessonData pipelines and vector stores11 min
  3. lessonRetrieval and prompt governance11 min
02Implementation and integration3 lessons · 32 min · quiz, 8 questions

Agents and tools, how a model is hosted, and how it sits inside the rest of the system. Domain: Implementation and Integration.

  1. lessonAgents and tools11 min
  2. lessonDeployment and model APIs11 min
  3. lessonEnterprise integration10 min
03Safety, security, and governance3 lessons · 31 min · quiz, 8 questions

Filter what goes in and out, protect the data, and keep a person on the decisions that need one. Domain: AI Safety, Security, and Governance.

  1. lessonInput and output safety10 min
  2. lessonData security and privacy11 min
  3. lessonGovernance and responsible use10 min
04Cost, performance, and monitoring3 lessons · 29 min · quiz, 8 questions

Spend tokens on purpose, keep latency inside the budget, and watch the system after it ships. Domain: Operational Efficiency and Optimization for GenAI Applications.

  1. lessonCost of generative applications10 min
  2. lessonPerformance9 min
  3. lessonMonitoring generative applications10 min
05Testing and troubleshooting3 lessons · 31 min · quiz, 8 questions

Score outputs before you trust them, and split a bad answer into retrieval, model, and platform. Domain: Testing, Validation, and Troubleshooting.

  1. lessonEvaluation11 min
  2. lessonTroubleshoot wrong answers10 min
  3. lessonTroubleshoot runtime failures10 min

Read the Generative AI Developer – Professional course

Sign up free to read the 15 lessons and take the 5 quizzes. The lab runtime is kubeadm clusters only, so this course does not include an AWS account.

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