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| Section | Weight | Objectives |
|---|---|---|
| AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Data Preparation for AI | - Data cleaning and transformation
| |
| Data for AI | - Data identification and governance
| |
| AI Model Development and Iteration | - Model building and validation
| |
| 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 # 311
You are working for a large multinational organization and have been assigned to a new project.
For your new ML project you need to make sure you're managing data privacy and security as you're working with sensitive customer data.
What critical security issues do you need to make sure you address (Select all that apply.):
Answer: A,B,C,D
NEW QUESTION # 312
In the early stages of an AI project, the team needs to determine the types of environments and devices where the AI solution will be used. This information is crucial to ensure a successful implementation. Which action should the project manager implement first?
Answer: D
Explanation:
Drafting a detailed usage scenario analysis identifies where, how, by whom, and on which devices the AI solution will be used. This provides the practical context needed to define environments, user interactions, deployment needs, and implementation constraints early in the project.
NEW QUESTION # 313
A project manager is defining the operationalization plan for a deployed model and must specify when retraining occurs.
What is the most effective approach?
Answer: B
Explanation:
Threshold-based triggers tie retraining to measured degradation rather than arbitrary intervals, avoiding both unnecessary retraining and prolonged periods of poor performance. Monitoring metrics against defined thresholds is a core operationalization control.
NEW QUESTION # 314
A financial institution is implementing a new AI system for fraud detection. The project team must ensure the data meets the needs of the AI solution by verifying data quality, completeness, and relevance. They have access to various internal and external data sources. Which method addresses the project team's objectives?
Answer: C
Explanation:
Conducting a comprehensive data audit and cleansing process verifies whether internal and external fraud-related data is accurate, complete, relevant, and usable for the AI solution before model development or operationalization.
NEW QUESTION # 315
A healthcare organization is preparing training data for an AI model that predicts patient readmissions. The team discovers inconsistent coding across clinics for the same diagnosis.
Which action best addresses the problem during data preparation?
Answer: D
Explanation:
PMI-CPMAI aligns data preparation with executing data cleansing and enhancement activities so that datasets meet model and operational requirements. Inconsistent clinical coding is a data quality issue that threatens accuracy, fairness, and interpretability, because identical conditions may be represented differently across sources. The PMI-aligned response is to determine and apply the necessary transformation steps--standardizing codes to a controlled vocabulary, mapping local codes to a canonical schema, normalizing formats, and documenting rules and lineage so the process is auditable.
NEW QUESTION # 316
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