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| Section | Weight | Objectives |
|---|---|---|
| Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Define data requirements and sources - Plan data collection, storage, and infrastructure |
| Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks - Manage bias, risk, compliance, and societal impact |
| Identify Business Needs and Solutions | 26% | - Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria |
| Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch |
| Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability |
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NEW QUESTION # 189
A project manager is tasked with ensuring the transparency of an AI project's data selection process. The project involves integrating multiple data sources from various departments. What should the project manager do?
Answer: B
Explanation:
Creating a comprehensive data catalog documents available data sources, ownership, definitions, lineage, quality, and selection rationale. This provides transparency into why specific departmental data was chosen and how it will be used in the AI project.
NEW QUESTION # 190
A development team is tasked with creating an AI system to assist physicians with diagnosing medical conditions. They encountered cases where symptoms do not always lead to well-defined diagnoses. Which approach should the project manager integrate to handle the inherent uncertainty?
Answer: B
Explanation:
Keeping a human in the loop ensures that physicians remain responsible for interpreting uncertain, ambiguous, or incomplete diagnostic outputs. This is especially important in medical decision-making, where symptoms may not map cleanly to a single diagnosis and clinical judgment is required.
NEW QUESTION # 191
An IT services company is verifying data quality for an AI project aimed at predicting server downtimes. The project manager needs to decide whether to proceed with data preparation.
Which technique should the project manager use?
Answer: B
Explanation:
Exploratory data analysis helps verify whether the server downtime data is complete, accurate, consistent, relevant, and suitable before moving into data preparation. It allows the project manager to identify missing values, outliers, patterns, anomalies, and quality issues that could affect model development.
NEW QUESTION # 192
Your team is starting a new facial recognition project and you want to ensure that the project is being done with Trustworthy AI in mind.
At what phase of CPMAI would Trustworthy AI be considered?
Answer: H
NEW QUESTION # 193
You're working on a project and are working with personally identifiable information (PII). What's the best approach to take when it comes to collecting and using this data?
Answer: C
NEW QUESTION # 194
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