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| Section | Weight | Objectives |
|---|---|---|
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Data Preparation for AI | - Data cleaning and transformation
| |
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| AI Model Development and Iteration | - Model building and validation
| |
| AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Data for AI | - Data identification and governance
|
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NEW QUESTION # 214
Your team has built a new robot that roams the halls at your organization and helps with various things such as small deliveries. However, you notice that many employees are opting not to use the robot. When you ask them why they tell you that the robot looks "creepy" and they would rather not interact with it.
What's going on here?
Answer: A
NEW QUESTION # 215
Your team has built a new robot that roams the halls at your organization and helps with various things such as small deliveries. However, you notice that many employees are opting not to use the robot. When you ask them why they tell you that the robot looks "creepy" and they would rather not interact with it. What's going on here?
Answer: A
Explanation:
The "Uncanny Valley" effect describes discomfort people feel when robots or AI systems look almost - but not quite - human, causing them to appear creepy and reducing user acceptance.
NEW QUESTION # 216
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: D
NEW QUESTION # 217
As an organization building an AI solution for your current customers based in NYC, but with possible plans for future expansion, how should you handle worldwide AI laws and regulations?
Answer: C
NEW QUESTION # 218
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: B
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 # 219
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