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| Section | Weight | Objectives |
|---|---|---|
| Managing AI | 8% | - Stakeholder management - Managing AI project teams and resources - Risk management in AI projects |
| 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 |
| Data for AI | 13% | - DataOps concepts - Data preparation and preprocessing - Data strategy and governance |
| Machine Learning | 13% | - Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision) |
| CPMAI Methodology | 41% | - Phase V: Operationalization
|
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NEW QUESTION # 14
The confusion matrix measures how the algorithm performs for a binary classification activity. As your team is running tests to evaluate model performance, they are seeing the model is incorrectly categorizing flowers as trees. Your model is provided the following:
Answer: B
Explanation:
Incorrectly categorizing flowers as trees represents false positives - instances where the model incorrectly predicts the positive class when it should not.
NEW QUESTION # 15
Your team is working on a new project for finding the most optimal flow of warehouse robots on the warehouse floor. Which type of machine learning approach would be most appropriate to pick for this problem?
Answer: C
Explanation:
Reinforcement learning is well-suited for optimizing sequential decision-making tasks like robot navigation, where agents learn optimal actions through interaction with the environment.
NEW QUESTION # 16
A project team is using a prompt engineering approach to improve AI/machine learning (ML) model outputs. They started with broad questions and then narrowed down the specific elements.
If the team had provided insufficient context, what would be the result?
Answer: D
Explanation:
PMI guidance on prompts and prompt engineering states that prompts "supply the system with context and guidance as well as constraints," and that the value of a GenAI system "can only be realized through the instructions provided to it." PMI further explains that while an AI system can respond to very short inputs, "the less specific a prompt, the more likely the results will be vague or unhelpful," explicitly linking insufficient specificity/context to degraded usefulness. In PMI's recommended "diverge and converge" prompt approach, teams begin broad, then progressively refine, adding details such as industry, region, project type, intended use, and examples-- because "the granularity of the input will be directly proportional to the utility of the output received." Therefore, if the team provides insufficient context, the model must "guess" what is intended, which most directly manifests as answers that are not aligned to the actual task needs (i.e., lacking relevance), rather than being more accurate or more efficient.
NEW QUESTION # 17
A healthcare organization plans to develop an AI-driven diagnostic tool. To define the required data, the project manager needs to ensure data consistency and accessibility. Which method should the project manager use?
Answer: B
Explanation:
Performing a data quality assessment with extraction, transformation, and loading processes helps ensure healthcare data is consistent, accessible, cleaned, transformed, and prepared in a usable format for the diagnostic AI tool.
NEW QUESTION # 18
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: B
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 # 19
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