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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Fundamentals | 16% | - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations |
| Topic 2: Data for AI | 13% | - Data strategy and governance - Data preparation and preprocessing - DataOps concepts |
| Topic 3: CPMAI Methodology | 41% | - Phase VI: Iteration & Monitoring
|
| Topic 4: Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning |
| Topic 5: Trustworthy AI | 9% | - Privacy and security - Transparency and explainability - Ethical considerations and bias |
| Topic 6: Managing AI | 8% | - Risk management in AI projects - Managing AI project teams and resources - Stakeholder management |
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NEW QUESTION # 84
An AI governance review requires ensuring that all model decisions can be traced and justified during audits. The organization must demonstrate accountability to regulators and stakeholders.
Which mechanism should the project manager implement to meet this requirement effectively?
Answer: A
Explanation:
Audit trails and documentation provide traceability, essential for governance and accountability.
NEW QUESTION # 85
Your team is tasked with selecting an algorithm for a supervised learning classification project.
Which algorithm might you choose?
Answer: A
Explanation:
K-nearest neighbor is a supervised learning algorithm used for classification tasks, making it suitable for supervised classification projects.
NEW QUESTION # 86
Different AI project team members are responsible for various parts of the project, both cognitive and non-cognitive. The project manager needs to ensure effective accountability documentation.
Which method will help to ensure accurate documentation?
Answer: A
Explanation:
Using a centralized documentation system gives all team members a single source of truth for responsibilities, decisions, progress, and accountability across both cognitive and noncognitive project work. This helps ensure documentation remains accurate, accessible, and consistently maintained.
NEW QUESTION # 87
You recently completed an image recognition project at your company that was focused on identifying different types of cars. You have now been assigned a new image recognition project that is focused on identifying different types of animals. You know you can shortcut model development by using a specific technique.
What is this technique called?
Answer: C
NEW QUESTION # 88
A project team is using a generative AI assistant to draft stakeholder communications. The drafts are often generic and miss project constraints. What is the most likely cause?
Answer: C
Explanation:
PMI guidance on using GenAI highlights that prompts must provide context, guidance, and constraints; otherwise outputs tend to be vague or unhelpful. If stakeholder communications miss constraints (scope boundaries, timeline, dependencies, risk posture), the most likely cause is insufficient prompt specificity--e.g., missing audience, intent, tone, project phase, constraints, and success criteria. PMI explains that the utility of GenAI outputs is strongly tied to the granularity of input: when prompts lack detail, results often become generic and misaligned with the real need.
In CPMAI-aligned execution, this is addressed by iteratively refining prompts (diverge then converge), adding structured context such as assumptions, constraints, and acceptance criteria, and validating outputs against governance expectations for accuracy and appropriateness.
NEW QUESTION # 89
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