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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing AI | 8% | - Managing AI project teams and resources - Stakeholder management - Risk management in AI projects |
| Topic 2: CPMAI Methodology | 41% | - Phase III: Algorithm Selection & Model Training
|
| Topic 3: Machine Learning | 13% | - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) |
| Topic 4: Data for AI | 13% | - Data preparation and preprocessing - Data strategy and governance - DataOps concepts |
| Topic 5: Trustworthy AI | 9% | - Ethical considerations and bias - Transparency and explainability - Privacy and security |
| Topic 6: AI Fundamentals | 16% | - Types of AI and Machine Learning - AI capabilities and limitations - Concepts and terminology of Artificial Intelligence |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. Before fully deploying a new recommendation algorithm, the team wants to evaluate its performance against the existing system using real users. The goal is to measure impact on engagement and business metrics. What technique should be used?
A) Feature engineering
B) Cross-validation
C) Data cleaning
D) A/B testing
2. A consulting firm is preparing data for an AI-driven customer segmentation model. They need to verify data quality before data preparation. What should the project manager do first?
A) Conduct data cleaning.
B) Implement data enhancement.
C) Assess data completeness.
D) Apply data labeling techniques.
3. Leadership wants a new HR system built that will better handle potential candidate matching. The project manager assigned to this project believes that the project is well-suited for AI, however they are unsure which pattern of AI this would be. What should the project manager do?
A) Determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
B) Move forward without determining which patter of AI this falls under.
C) Conduct a straw poll with stakeholders to determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
D) Pick an algorithm that seems best suited for the problem and then determine which pattern of AI it is based on the algorithm selected.
4. A financial services firm is building an AI model to detect fraudulent transactions. Identifying and validating data sources is critical to the model's success. What is an effective method that helps to ensure data accuracy?
A) Employing a federated database system for decentralized data access
B) Setting up a batch processing system for data cleansing
C) Utilizing data lineage tools to track data origin and transformations
D) Implementing a blockchain-based ledger for transaction data
5. An AI model trained on historical transaction data performs well initially. Over time, customer behavior changes due to market conditions, causing declining prediction accuracy. The team observes a mismatch between training and current data distributions. What is this issue called?
A) Concept drift
B) Underfitting
C) Data leakage
D) Overfitting
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: A |
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