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| Section | Weight | Objectives |
|---|---|---|
| Data for AI | - Data identification and governance
| |
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| AI Model Development and Iteration | - Model building and validation
| |
| Data Preparation for AI | - Data cleaning and transformation
| |
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
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NEW QUESTION # 275
An organization is considering multiple AI solutions to ensure regulatory compliance. What is an effective technique that helps to ensure the AI solution adheres to compliance requirements?
Answer: A
Explanation:
Continuous compliance monitoring provides ongoing oversight of the AI solution against applicable regulatory requirements. It helps identify compliance deviations as they occur and supports timely corrective action as regulations, data, models, and system behavior change.
NEW QUESTION # 276
An AI model trained on historical transaction data performs well initially. Over time, customer behavior changes due to market conditions, causing declining prediction accuracy. The team observes a mismatch between training and current data distributions. What is this issue called?
Answer: C
Explanation:
Concept drift occurs when data patterns change over time, degrading model performance.
NEW QUESTION # 277
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
Explanation:
It is essential to comply with relevant data, privacy, and AI laws not only in the current operating region but also in regions targeted for expansion, ensuring legal readiness and avoiding future compliance risks.
NEW QUESTION # 278
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: A
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 # 279
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 # 280
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