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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Trustworthy AI9%- Transparency and explainability
- Ethical considerations and bias
- Privacy and security
Topic 2: Data for AI13%- Data preparation and preprocessing
- Data strategy and governance
- DataOps concepts
Topic 3: Machine Learning13%- Supervised, Unsupervised, and Reinforcement Learning
- Deep Learning and Neural Networks
- Algorithms and models (e.g., NLP, Computer Vision)
Topic 4: Managing AI8%- Risk management in AI projects
- Stakeholder management
- Managing AI project teams and resources
Topic 5: CPMAI Methodology41%- Phase I: Problem Identification
  • 1. Evaluate initial AI feasibility
  • 2. Identify business needs and solutions
- Phase V: Operationalization
  • 1. Plan for deployment and integration
  • 2. Define success criteria and monitoring
- Phase VI: Iteration & Monitoring
  • 1. Retraining and model drift management
  • 2. Continuous monitoring and iteration
- Phase III: Algorithm Selection & Model Training
  • 1. Manage model training and configuration
  • 2. Oversee AI/ML model technique selection
- Phase II: Data Identification & Curation
  • 1. Identify data needs
  • 2. Assess data readiness and quality
- Phase IV: Model Evaluation
  • 1. Evaluate model performance against metrics
  • 2. Oversee AI/ML model QA/QC
Topic 6: AI Fundamentals16%- Concepts and terminology of Artificial Intelligence
- AI capabilities and limitations
- Types of AI and Machine Learning

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Free PDF 2026 PMI Perfect CPMAI: Cognitive Project Management in AI (PMI-CPMAI) Valid Test Book

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q254-Q259):

NEW QUESTION # 254
A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data centric projects.
What's the next best course of action?

Answer: C


NEW QUESTION # 255
A financial services firm is integrating AI to enhance fraud detection. To oversee data evaluation, the project manager needs to ensure the integrity and accuracy of input data, including transaction histories and customer profiles. Which method provides the results that address the requirements?

Answer: D

Explanation:
Using a fact checklist provides a systematic way to verify the integrity, accuracy, completeness, and reliability of transaction histories and customer profiles before they are used by the fraud detection model.


NEW QUESTION # 256
An AI project must comply with strict data protection and industry regulations. The project manager needs to ensure all processes align with legal requirements throughout the lifecycle.
Which stakeholder is most critical to involve continuously?

Answer: A

Explanation:
Legal/compliance ensures adherence to regulatory requirements.


NEW QUESTION # 257
Your team is planning an AI-enabled chatbot project to help reduce call center load. They are currently determining if the project can get off the ground and working through the AI Go/No Go feasibility questions.
What stage of CPMAI is the team currently working on?

Answer: E


NEW QUESTION # 258
An organization's leadership team is concerned about the ethical implications of operationalizing their AI model. How should the project manager address these concerns in their presentation to the team?

Answer: C

Explanation:
Demonstrating the use of bias detection tools directly addresses ethical concerns by showing how the project team evaluates fairness, identifies potentially discriminatory outcomes, and supports responsible operationalization of the AI model.


NEW QUESTION # 259
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