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| Section | Weight | Objectives |
|---|---|---|
| Data for AI | - Data identification and governance
| |
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| AI Model Development and Iteration | - Model building and validation
| |
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Data Preparation for AI | - Data cleaning and transformation
| |
| AI System Testing and Evaluation | - Model evaluation and monitoring
|
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NEW QUESTION # 226
An IT services company project manager is creating an AI project scope statement. They need to include details on the environments, devices, and personnel that will use the AI solution. What should the project manager do?
Answer: D
Explanation:
Developing a comprehensive usage scenario analysis defines how the AI solution will be used, including the operating environments, devices, user roles, workflows, and personnel involved.
This provides the practical context needed for a complete AI project scope statement.
NEW QUESTION # 227
A consulting firm is determining the feasibility of an AI project. They need to justify the use of AI over noncognitive solutions. The project manager has listed potential noncognitive alternatives.
What is an effective method to support an AI approach?
Answer: D
NEW QUESTION # 228
A project team is tasked with ensuring all AI-related decisions and actions are documented comprehensively for future auditing purposes. They need to track the reasons for specific AI choices, their impacts, and any issues encountered during the implementation. What is represented in this situation?
Answer: A
Explanation:
Transparency is represented because the team is documenting AI decisions, rationale, impacts, and implementation issues so future reviewers can understand how and why choices were made.
This supports auditability, accountability, and traceability across the AI project.
NEW QUESTION # 229
A healthcare provider plans to deploy an AI system to predict patient readmissions. The project manager needs to conduct a risk assessment to ensure patient safety and data integrity. What is an effective method to help ensure the AI system adheres to ethical standards?
Answer: D
Explanation:
PMI guidance for responsible and trustworthy AI stresses that ethical performance is not a one- time checkbox; it requires ongoing oversight, including transparency, accountability, and continuous controls. PMI-CPMAI's exam outline explicitly highlights maintaining audit trails for algorithmic decision-making, implementing compliance monitoring mechanisms, and managing accountability documentation--foundational practices that align directly with continuous monitoring and auditing. In high-stakes healthcare use cases like readmission prediction, model drift, data drift, and shifting patient populations can degrade performance and fairness over time, which can create patient safety risks. Continuous monitoring enables the team to detect deteriorating accuracy, emerging bias, and unexpected failure modes early; auditing supports traceability of decisions, data lineage, and adherence to governance requirements. PMI also emphasizes that ethical AI demands validation and transparency, noting that accountability and continuous monitoring are crucial to maintain ethical standards and minimize undesirable outcomes.
NEW QUESTION # 230
A machine learning model shows excellent performance on training data but significantly worse results on validation datasets. The team suspects the model memorizes patterns rather than generalizing effectively. Which technique should be applied to address this issue?
Answer: C
Explanation:
Cross-validation helps evaluate generalization and detect overfitting.
NEW QUESTION # 231
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