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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Topic 2: Data Preparation for AI | - Data cleaning and transformation
| |
| Topic 3: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Topic 4: AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Topic 5: Data for AI | - Data identification and governance
| |
| Topic 6: AI Model Development and Iteration | - Model building and validation
|
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NEW QUESTION # 12
Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs.
What's the most critical role to staff for in the Big Data / Data Engineering Environment?
Answer: A
NEW QUESTION # 13
A project manager is preparing a contingency plan for an AI-enabled underwriting platform.
During outages, the business must still make time-sensitive decisions. What strategy best supports business continuity?
Answer: D
Explanation:
PMI-CPMAI highlights the need to manage AI operational risks through structured contingency planning and trustworthy AI governance. A business continuity-aligned contingency strategy is a manual override with clear escalation and decision rules so critical underwriting decisions can continue when the AI platform is unavailable. This is consistent with CPMAI expectations for operational readiness and accountability: define alternate operating modes, ensure decision traceability, and maintain service reliability despite disruptions. Stopping all underwriting (B) fails the "must still decide" requirement.
NEW QUESTION # 14
Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same. What is most likely happening here?
Answer: A
Explanation:
Biased datasets can cause the model to discriminate against certain demographics, resulting in unfair or unequal loan denial decisions despite similar attributes.
NEW QUESTION # 15
Accompany wants to determine whether to move forward with an AI project. The project manager has answered and received approval for all questions on technology, data, and business feasibility. Which activity did the project manager perform?
Answer: D
Explanation:
A go/no-go assessment evaluates whether the AI project should proceed by confirming technology readiness, data feasibility, and business value. Approval across these areas indicates the project manager completed the decision checkpoint needed to move forward.
NEW QUESTION # 16
Before developing a complex model, a project team builds a simple rules-based version of the solution using existing business logic.
What is the primary purpose of this?
Answer: C
Explanation:
A baseline defines the performance level achievable without machine learning. If a complex model does not meaningfully exceed the baseline, the additional development, infrastructure, and maintenance cost cannot be justified.
NEW QUESTION # 17
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