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| Section | Weight | Objectives |
|---|---|---|
| Data Preparation for AI | - Data cleaning and transformation
| |
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Data for AI | - Data identification and governance
| |
| AI Model Development and Iteration | - Model building and validation
|
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NEW QUESTION # 235
A model has passed evaluation and is approved for production. The project team has no defined process for versioning models, tracking which data version produced them, or rolling back a deployment.
What does this project lack?
Answer: C
Explanation:
Model versioning, data lineage, reproducibility, and rollback capability are core MLOps practices supporting the Model Operationalization phase. Without them, the organization cannot reliably reproduce, audit, or recover a deployed model.
NEW QUESTION # 236
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: B
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 # 237
A project team is evaluating whether an AI initiative should proceed beyond discovery.
Stakeholders are aligned on objectives, but the team has not confirmed data access, quality, or legal constraints.
What is the most appropriate next action?
Answer: B
Explanation:
PMI-CPMAI explicitly includes conducting AI go/no-go assessments as a gated decision mechanism to determine whether conditions are sufficient to proceed. In CPMAI-aligned practice, stakeholder alignment on objectives is necessary but not sufficient; readiness must also cover data availability, permissions, privacy/legal constraints, and the feasibility of meeting acceptable performance metrics. A go/no-go assessment brings these prerequisites into a structured review, allowing the project manager to document assumptions, identify critical gaps (e.g., data rights, retention limits, PII handling), and decide whether to proceed, pivot, or stop before incurring avoidable cost and rework.
NEW QUESTION # 238
Your team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, your team doesn't have access to all the data it needs. What's the best course of action?
Answer: C
Explanation:
Moving forward cautiously while recognizing potential pauses allows progress with flexibility, enabling the team to adapt when data access issues arise without derailing the entire project.
NEW QUESTION # 239
Your team has created a model that is going to be used for monitoring systems and it needs to provide analysis on a weekly basis. What's the most appropriate Model Operationalization approach?
Answer: A
Explanation:
Batch prediction suits scenarios needing periodic analysis, like weekly monitoring, by processing accumulated data in scheduled batches.
NEW QUESTION # 240
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