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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Topic 2: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Topic 3: AI Model Development and Iteration | - Model building and validation
| |
| Topic 4: Data Preparation for AI | - Data cleaning and transformation
| |
| Topic 5: Data for AI | - Data identification and governance
| |
| Topic 6: AI System Testing and Evaluation | - Model evaluation and monitoring
|
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NEW QUESTION # 27
A team is getting ready to begin working on a ML project. They need to build a data preparation pipeline and someone on the team suggests they reuse the same pipeline they created for their last project.
What's wrong with this suggestion?
Answer: A
NEW QUESTION # 28
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 # 29
A fintech AI project uses third-party data sources for credit risk modeling. The project manager is concerned about compliance and accountability if the external data quality changes. Which control best supports responsible and trustworthy AI delivery?
Answer: A
Explanation:
PMI's trustworthy AI framing highlights governance, transparency, and accountability as essential ingredients for systems people can interpret and monitor. When third-party data feeds can change, the PMI-aligned approach is to establish governance and supplier controls that define data quality expectations, lineage, permitted uses, privacy constraints, and monitoring/audit mechanisms. This supports accountability by making data dependencies explicit and enabling early detection when upstream changes degrade model behavior.
NEW QUESTION # 30
An aerospace company is operationalizing an AI system to optimize flight routes. The system must be safe and comply with aviation regulations. The project team is evaluating methods to prevent potential safety risks.
Which method should the project manager use to prevent safety risk issues?
Answer: B
Explanation:
Extensive simulation testing exposes the AI system to a wide range of normal, abnormal, and edge-case flight conditions before operational use. This helps identify unsafe behaviors and failure modes early, allowing the team to address safety risks before they affect real-world flight operations.
NEW QUESTION # 31
An AI solution performs well in testing but fails after deployment due to differences between training data and real-world production data. The team identifies discrepancies in feature distributions across environments. What is the primary issue affecting performance?
Answer: D
Explanation:
Differences between training and production data distributions cause performance degradation.
NEW QUESTION # 32
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