Prepare with PMI CPMAI exam practice material, pass for sure

Updated: Aug 24, 2026

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PMI CPMAI Exam Overview:

Certification Vendor:PMI
Exam Name:PMI Certified Professional in Managing AI (PMI-CPMAI)
Exam Number:PMI-CPMAI
Passing Score:Pass/Fail (Not publicly disclosed)
Exam Duration:160 minutes
Exam Format:Multiple Choice, Scenario-based
Certificate Validity Period:3 Years (Maintenance: 30 PDUs)
Exam Price:$699 (PMI Member) / $899 (Non-Member)
Related Certifications:PMP
PMI-ACP
Available Languages:English, Arabic, Brazilian Portuguese, Chinese (Simplified), Chinese (Traditional), French, German, Japanese, Korean, Spanish (LATAM)
Real Exam Qty:120
Sample Questions:PMI CPMAI Sample Questions
Exam Way:Online Proctored / Test Center
Pre Condition:Training course completion required prior to exam.
Official Syllabus URL:https://www.pmi.org/certifications/ai-project-management-cpmai

PMI CPMAI Exam Syllabus Topics:

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

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

1. Multiple teams are developing AI solutions independently, resulting in inconsistent ethical practices and governance standards. Leadership wants to ensure alignment across all projects.
What is the best action for the project manager to recommend?

A) Allow teams full autonomy
B) Increase model complexity
C) Focus only on technical performance
D) Establish an AI governance framework and policies


2. An IT services company is working on a project to develop an AI-based customer support system.
During data preparation, the project manager needs to clean and transform customer interaction logs. What is an effective technique to handle any missing data?

A) Duplicate existing data to fill in missing gaps.
B) Ignore missing data if it seems insignificant.
C) Fill missing values with zeros without analysis.
D) Remove records with missing values if minimal.


3. For AI projects the code and systems don't matter as much as the data. In fact, big data is what's powering much of this latest wave of AI. What's most important for your company to consider around data?

A) Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.
B) Understanding which algorithms are best for your data needs.
C) Collect enormous amounts of data - the more data the better.
D) Have team members that have experience, understanding of tools, and the ability to deal with massive volumes of data.


4. Upper management is looking to roll out a new product and wants to see if there are any patterns and insights that can be discovered from customer data. Your team has been tasked to discover these potential patterns and structures within this data. Which type of machine learning approach would be most appropriate to pick for this problem?

A) All would work equally well
B) Supervised Learning
C) Unsupervised Learning
D) Reinforcement Learning


5. An AI governance review requires ensuring that all model decisions can be traced and justified during audits. The organization must demonstrate accountability to regulators and stakeholders.
Which mechanism should the project manager implement to meet this requirement effectively?

A) Increase model complexity
B) Reduce dataset size
C) Automate deployment pipelines
D) Maintain model documentation and audit trails


Solutions:

Question # 1
Answer: D
Question # 2
Answer: D
Question # 3
Answer: D
Question # 4
Answer: C
Question # 5
Answer: D

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