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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning |
| Trustworthy AI | 9% | - Privacy and security - Transparency and explainability - Ethical considerations and bias |
| AI Fundamentals | 16% | - AI capabilities and limitations - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence |
| Managing AI | 8% | - Risk management in AI projects - Managing AI project teams and resources - Stakeholder management |
| Data for AI | 13% | - DataOps concepts - Data preparation and preprocessing - Data strategy and governance |
| CPMAI Methodology | 41% | - Phase III: Algorithm Selection & Model Training
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. A company has recently received a large number of spam emails. Some employees have been clicking on the suspicious emails causing challenges for their IT department. The head of IT wants to create a more robust spam filter and has tasked a team with this project. Which type of algorithm should the project team use?
A) Regression
B) Clustering
C) Binary (or Binomial) Classification
D) Multiclass Classification
2. An organization aims to improve transparency and trust in its AI systems. Stakeholders request documentation outlining model purpose, limitations, risks, and performance characteristics. Which artifact should the project manager ensure is created and maintained?
A) Data pipeline diagram
B) Source code repository
C) Model card
D) Deployment script
3. 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) Overfitting
B) Underfitting
C) Concept drift
D) Data leakage
4. At the start of an AI initiative, stakeholders are unclear about desired outcomes and success criteria. The team risks building a technically sound model that fails to deliver business value.
Which lifecycle phase should address this issue?
A) Deployment
B) Model optimization
C) Monitoring
D) Problem framing
5. A project team is currently evaluating an AI solution. They need to ensure the machine learning model provides the expected business benefits. Which critical factor should the project manager assess?
A) Maximization of model interpretability
B) Minimization of human intervention
C) Alignment with key performance indicators
D) Volume of training data
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: C |
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