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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Fundamentals | 16% | - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning - AI capabilities and limitations |
| Topic 2: Managing AI | 8% | - Managing AI project teams and resources - Stakeholder management - Risk management in AI projects |
| Topic 3: Trustworthy AI | 9% | - Transparency and explainability - Privacy and security - Ethical considerations and bias |
| Topic 4: Data for AI | 13% | - Data strategy and governance - DataOps concepts - Data preparation and preprocessing |
| Topic 5: CPMAI Methodology | 41% | - Phase VI: Iteration & Monitoring
|
| Topic 6: Machine Learning | 13% | - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) |
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NEW QUESTION # 135
A project team is evaluating whether an AI initiative should proceed beyond discovery.
Stakeholders are aligned on objectives, but the team has not confirmed data access, quality, or legal constraints.
What is the most appropriate next action?
Answer: A
Explanation:
PMI-CPMAI explicitly includes conducting AI go/no-go assessments as a gated decision mechanism to determine whether conditions are sufficient to proceed. In CPMAI-aligned practice, stakeholder alignment on objectives is necessary but not sufficient; readiness must also cover data availability, permissions, privacy/legal constraints, and the feasibility of meeting acceptable performance metrics. A go/no-go assessment brings these prerequisites into a structured review, allowing the project manager to document assumptions, identify critical gaps (e.g., data rights, retention limits, PII handling), and decide whether to proceed, pivot, or stop before incurring avoidable cost and rework.
NEW QUESTION # 136
You're being told by upper management that you need to manage a new AI project. You need to determine the AI project fit to make sure you're actually solving a real business problem. During phase I: Business Understanding, you should consider at least one of the following (Choose all that apply):
Answer: C,D,E,F
Explanation:
AI projects should focus on enhancing revenue, solving previously unsolved problems, improving competitiveness, or solving existing problems more efficiently to ensure real business value.
NEW QUESTION # 137
You are being tasked to manage an AI project at your company and you need to identify which project to start with. What's the best way to approach this?
Answer: B
NEW QUESTION # 138
One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts. What course of action might make the issue worse?
Answer: C
Explanation:
Expanding the scope when facing data collection issues can worsen problems by increasing complexity and resource demands, making it harder to manage and deliver the project successfully.
NEW QUESTION # 139
Business stakeholders reviewing a deployed classification model ask why a specific customer record received its prediction. The model is a gradient boosted ensemble.
Which approach best addresses this request?
Answer: B
Explanation:
Post-hoc explanation techniques attribute an individual prediction to the contribution of each input feature, addressing instance-level questions without replacing the model. Aggregate performance metrics describe overall behavior but cannot explain a single decision.
NEW QUESTION # 140
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