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Start practicePMI Certified Professional in Managing AI
The PMI Certified Professional in Managing AI (PMI-CPMAI) validates the ability to run and manage AI, machine learning, and data-science projects using the vendor-agnostic, data-centric, iterative CPMAI methodology. It covers identifying business needs and AI solutions, data needs, model development and evaluation, operationalizing AI, and supporting responsible and trustworthy AI.
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About this exam
CPMAI (Cognitive Project Management in AI) is a vendor-agnostic, data-centric, AI-specific, iterative methodology for running AI, machine learning, and cognitive technology projects. Built on and extending CRISP-DM, it runs AI initiatives through six phases (Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Operationalization) in short iterative cycles, with responsible and trustworthy AI applied throughout. PMI acquired Cognilytica in 2024 and offers CPMAI as the PMI Certified Professional in Managing AI.
Who should take this exam
Project managers, product managers, data and AI practitioners, business analysts, and leaders who plan, run, or oversee AI, machine learning, and data-science initiatives. No prior project management, technical, or AI experience is required to take the exam.
Career benefits
Demonstrates the ability to manage AI projects end to end using a proven, vendor-agnostic methodology, reducing the high failure rate of AI initiatives. CPMAI supports roles such as AI Project Manager, AI Product Manager, and AI Program Lead.
How to prepare
Complete the PMI-CPMAI exam prep course and gain familiarity with the six-phase CPMAI methodology, the seven patterns of AI, data-centric AI project management, model evaluation against business goals, operationalization/MLOps, and responsible AI. PMI provides an official exam content outline and course.
Quick facts
Exam costBundled with the PMI-CPMAI course (varies by region)
Valid forRenew via PMI continuing certification requirements (PDUs)
Length90 minutes
Questions on exam100
Passing scoreScaled score (pass/fail)
FormatMultiple choice and scenario-based questions.
Practice questions200
Objectives5
Official pageView
What's covered
1. Support Responsible and Trustworthy AI Efforts
15%- 1a Apply responsible AI principles (fairness, transparency, accountability, privacy, safety)
- 1b Identify and mitigate AI risk, bias, and ethical concerns
- 1c Ensure AI governance, compliance, and trust across the lifecycle
2. Identify Business Needs and Solutions
26%- 2a Determine whether AI is the right approach (AI go/no-go, the seven patterns of AI)
- 2b Define the business problem, objectives, success criteria, and ROI
- 2c Establish project scope, feasibility, and stakeholder alignment (Business Understanding)
- 2d Apply the CPMAI methodology and its iterative, agile approach
3. Identify Data Needs
26%- 3a Determine data requirements for the AI approach (Data Understanding)
- 3b Assess data availability, quality, quantity, and sources
- 3c Prepare, clean, label, and transform data (Data Preparation)
- 3d Address data governance, bias, and privacy
4. Manage AI Model Development and Evaluation
16%- 4a Select and develop the AI/ML model approach (Modeling)
- 4b Train, tune, and iterate models
- 4c Evaluate model performance against business success criteria (Evaluation)
- 4d Decide whether to iterate, pivot, or proceed
5. Operationalize AI Solution
17%- 5a Deploy and integrate the AI solution into production (Operationalization)
- 5b Monitor, maintain, and govern models in production (MLOps, drift, retraining)
- 5c Plan for scaling and continuous improvement across iterations
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