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| Section | Weight | Objectives |
|---|---|---|
| Data for AI | - Data identification and governance
| |
| AI Model Development and Iteration | - Model building and validation
| |
| AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Data Preparation for AI | - Data cleaning and transformation
| |
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
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NEW QUESTION # 57
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 # 58
A project manager is considering different project management approaches for an AI solution deployment. They need to ensure the approach allows for iterative improvements and accommodates changing requirements. Which approach is effective in this situation?
Answer: C
Explanation:
An adaptive/agile approach supports iterative development, frequent feedback, and ongoing refinement. This is effective for AI solution deployment because model behavior, data needs, stakeholder expectations, and operational requirements may change as the project progresses.
NEW QUESTION # 59
An aerospace firm is developing an AI system for predictive maintenance of their aircraft. The project team needs to define the required data to train the model. Which activity should the project manager implement?
Answer: C
Explanation:
Developing a comprehensive data collection strategy defines what aircraft sensor, maintenance, operational, failure, and environmental data is required to train the predictive maintenance model.
This ensures the model is built from relevant and sufficient data before later preparation, streaming, or testing activities begin.
NEW QUESTION # 60
Use cognitive technologies/AI when you can't code the rules or you can't scale easily with people or automation. As a good rule of thumb when deciding if AI is right for the project you should:
Answer: B
NEW QUESTION # 61
During Model Evaluation, a project team observes that a model performs substantially better on training data than on the holdout test set.
What does this indicate, and what is the appropriate response?
Answer: B
Explanation:
A large gap between training and test performance indicates the model has learned noise specific to the training data rather than generalizable patterns. Regularization, reducing model complexity, or increasing training data volume are standard mitigations.
NEW QUESTION # 62
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