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PMI CPMAI Exam Overview:
| Certification Vendor: | Project Management Institute (PMI) |
| Exam Name: | PMI Cognitive Project Management in AI (PMI-CPMAI) Certification Exam |
| Exam Number: | PMI-CPMAI |
| Real Exam Qty: | 120 (100 scored, 20 unscored pretest) |
| Exam Duration: | 160 minutes |
| Passing Score: | Pass/Fail; no fixed numerical score published |
| Exam Format: | Multiple-choice, Scenario-based, Application-focused |
| Available Languages: | English |
| Certificate Validity Period: | Valid indefinitely; requires 30 PDUs every 3 years for maintenance |
| Related Certifications: | PMP® PMI-ACP® PMI-RMP® PMI-SP® |
| Exam Price: | USD 699 (PMI Member), USD 899 (Non-Member) |
| Recommended Training: | PMI-CPMAI Exam Prep Course |
| Exam Registration: | Pearson VUE Scheduling PMI Official Registration |
| Sample Questions: | PMI CPMAI Sample Questions |
| Exam Way: | Computer-based test (CBT) at Pearson VUE test center or online proctored |
| Pre Condition: | Completion of official PMI-CPMAI Exam Prep Course required; no prior experience or other certifications mandatory |
| Official Syllabus URL: | https://www.pmi.org/certifications/ai-project-management-cpmai |
PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability |
| Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch |
| Identify Business Needs and Solutions | 26% | - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria |
| Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Define data requirements and sources - Plan data collection, storage, and infrastructure |
| Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. A financial services organization deploys an AI credit-scoring model. Post-deployment monitoring reveals systematic underprediction for a protected demographic group, potentially affecting loan approvals. Stakeholders are concerned about fairness and compliance risks. What should the project manager prioritize FIRST?
A) Conduct a bias and fairness audit
B) Pause the system indefinitely
C) Replace the algorithm entirely
D) Retrain the model with additional data
2. You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing. What type of problem is this?
A) You are underfitting the data
B) You are overfitting the data
C) You have selected the wrong algorithm
D) You have selected the wrong data
3. You are working for a large multinational organization and have been assigned to a new project.
For your new ML project, you need to make sure you're managing data privacy and security as you're working with sensitive customer data. What critical security issues do you need to make sure you address? (Choose all that apply.)
A) Securing data at rest
B) Securing model data and metadata
C) Securely storing all data collected for training purposes
D) Compliance with Data Privacy Laws even if they are out of your physical jurisdiction
4. Your team was given a large dataset and has been tasked with organizing the data by type to make better insights from the results. You are facing problems with the approach that the previous project lead used which was a regression algorithm. What type of algorithm is the best approach for this project?
A) Multiclass Classification
B) Binary (or Binomial) Classification
C) Regression
D) Clustering
5. Your team is testing the NLP model they just created to make sure it's performing as expected.
Some of your team members want to move this model to production and move to the next iteration. What's wrong with this workflow?
A) Nothing is wrong with this workflow. You can move to the next iteration
B) You need to make sure the AI Go/No Go questions have been addressed
C) Model Evaluation requires continuous model evaluation, retraining, and operationalization
D) Team members should not be able to move to new projects until senior management signs off
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: A,B,C,D | Question # 4 Answer: D | Question # 5 Answer: C |
By Gale
