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| Section | Weight | Objectives |
|---|---|---|
| Data for AI | 13% | - Data strategy and governance - Data preparation and preprocessing - DataOps concepts |
| Managing AI | 8% | - Managing AI project teams and resources - Risk management in AI projects - Stakeholder management |
| AI Fundamentals | 16% | - AI capabilities and limitations - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence |
| Trustworthy AI | 9% | - Ethical considerations and bias - Transparency and explainability - Privacy and security |
| Machine Learning | 13% | - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) |
| CPMAI Methodology | 41% | - Phase V: Operationalization
|
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NEW QUESTION # 24
A company is evaluating whether to implement AI for a project. They have defined their business objectives and determined the AI capability they want to use. Which action will enable the project manager to move forward with the project?
Answer: A
Explanation:
Conducting a go/no-go assessment confirms whether the AI project should proceed after the business objectives and desired AI capability have been defined. It evaluates readiness, feasibility, value, risks, and whether AI is the appropriate solution before committing further resources.
NEW QUESTION # 25
An insurance company is selecting an AI approach to automate simple claim approvals for low- risk cases. The organization wants the system to take actions with minimal human intervention based on predefined policies. Which AI capability best fits?
Answer: B
Explanation:
In PMI's Seven Patterns of AI, capability selection depends on whether the system is primarily advising humans or acting on their behalf. When the goal is to automate operational actions-- approving or routing claims under policy constraints with minimal human intervention--the capability aligns with autonomous systems, which emphasize automated execution within defined rules, safeguards, and operational boundaries. Predictive analytics (B) can score risk, but it typically supports decision support; autonomous systems extend this by taking actions automatically according to governance-approved policies. PMI-CPMAI's responsible and trustworthy AI principles reinforce that higher-autonomy use cases require stronger controls: clear escalation paths, contingency plans, monitoring, and audit trails to ensure accountability for automated decisions.
NEW QUESTION # 26
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: B
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 # 27
Your team is ready to operationalize the model they have been working on. It's a model that is meant to be used on an "edge device", specifically a mobile phone and the user may sometimes be in remote locations without regular access to the internet. What's the most important thing to consider here?
Answer: C
Explanation:
For an edge device like a mobile phone used in remote locations with limited internet, the model must live on the device itself to ensure availability and functionality without relying on an internet connection.
NEW QUESTION # 28
Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned.
What would likely have helped avoid this conflict?
Answer: C
NEW QUESTION # 29
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