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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing AI | 8% | - Risk management in AI projects - Managing AI project teams and resources - Stakeholder management |
| Topic 2: Data for AI | 13% | - Data preparation and preprocessing - Data strategy and governance - DataOps concepts |
| Topic 3: Machine Learning | 13% | - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision) - Supervised, Unsupervised, and Reinforcement Learning |
| Topic 4: Trustworthy AI | 9% | - Transparency and explainability - Ethical considerations and bias - Privacy and security |
| Topic 5: CPMAI Methodology | 41% | - Phase II: Data Identification & Curation
|
| Topic 6: AI Fundamentals | 16% | - AI capabilities and limitations - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning |
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NEW QUESTION # 320
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 # 321
Major factors for the project you are currently working on is around the training time, cost, and complexity of training your models. Which algorithm is not the best choice given these constraints?
Answer: C
Explanation:
Neural networks typically require more training time, computational resources, and complexity compared to other algorithms, making them less suitable when training time, cost, and complexity are major constraints.
NEW QUESTION # 322
Your team is running a simulation-based optimization exercise to increase routing efficiency.
Learning for this exercise is done through "trial and error" Which type of machine learning approach is being leveraged for this exercise?
Answer: B
Explanation:
Reinforcement learning involves learning optimal actions through trial and error by interacting with the environment, making it suitable for simulation-based optimization.
NEW QUESTION # 323
A company is operationalizing an AI system for automated customer service. The system appears to favor queries in one language over others, despite having been trained on a multilingual dataset. Which two issues should the project lead investigate? (Choose two.)
Answer: A,B
Explanation:
Uneven preprocessing can cause one language to be cleaned, tokenized, normalized, or encoded differently from others, leading to biased performance. Checking the distribution of training samples per language helps determine whether the model had enough balanced representation to learn each language effectively.
NEW QUESTION # 324
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: A,B,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 # 325
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