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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI Model Development and Iteration | - Model building and validation
| |
| Data for AI | - Data identification and governance
| |
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Data Preparation for AI | - Data cleaning and transformation
| |
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| AI System Testing and Evaluation | - Model evaluation and monitoring
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. You've built your model and now need to see if it actually works as expected. In which phase of CPMAI is this done?
A) Phase I
B) Phase III
C) Phase II
D) Phase VI
E) Phase V
F) Phase IV
2. A consulting firm is determining the feasibility of an AI project. They need to justify the use of AI over noncognitive solutions. The project manager has listed potential noncognitive alternatives.
What is an effective method to support an AI approach?
A) Conducting a cost-benefit analysis comparing AI and noncognitive solutions
B) Emphasizing the simplicity and reliability of noncognitive solutions
C) Focusing on the novelty and technological AI appeal
D) Relying only on industry trends favoring AI adoption
3. A project team plans to use customer records containing names, national identification numbers, and health information to train a model. The records were originally collected for billing purposes.
What is the primary concern the project manager must address?
A) The dataset may be too small for reliable training
B) Health information may reduce model accuracy
C) Purpose limitation and lawful basis for the secondary use of personal data
D) The records may require reformatting before ingestion
4. You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?
A) Governed AI
B) Explainable AI
C) Responsible AI
D) Ethical AI
E) Transparent AI
5. A project team has a modest dataset and is concerned that a single train-test split may produce an unreliable performance estimate.
What approach should the project manager recommend?
A) Repeat training with different random seeds
B) Increase the proportion of data allocated to testing
C) Use the full dataset for both training and evaluation
D) Apply k-fold cross-validation
Solutions:
| Question # 1 Answer: E | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: E | Question # 5 Answer: D |
