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| Section | Weight | Objectives |
|---|---|---|
| AI Model Development and Iteration | - Model building and validation
| |
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Data for AI | - Data identification and governance
| |
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Data Preparation for AI | - Data cleaning and transformation
|
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NEW QUESTION # 310
A project team is using a generative AI assistant to draft stakeholder communications. The drafts are often generic and miss project constraints. What is the most likely cause?
Answer: D
Explanation:
PMI guidance on using GenAI highlights that prompts must provide context, guidance, and constraints; otherwise outputs tend to be vague or unhelpful. If stakeholder communications miss constraints (scope boundaries, timeline, dependencies, risk posture), the most likely cause is insufficient prompt specificity--e.g., missing audience, intent, tone, project phase, constraints, and success criteria. PMI explains that the utility of GenAI outputs is strongly tied to the granularity of input: when prompts lack detail, results often become generic and misaligned with the real need.
In CPMAI-aligned execution, this is addressed by iteratively refining prompts (diverge then converge), adding structured context such as assumptions, constraints, and acceptance criteria, and validating outputs against governance expectations for accuracy and appropriateness.
NEW QUESTION # 311
In a clustering analysis for data use, the project team finds that the clusters are not meaningful and do not provide actionable insights. Which activity should the project manager do with the project team?
Answer: A
Explanation:
Meaningless clusters often indicate that the available data lacks relevant features, has quality issues, or does not adequately represent the patterns needed for useful segmentation. Identifying data gaps and correcting deficiencies helps improve the usefulness and actionability of the clustering results.
NEW QUESTION # 312
An AI project for a financial technology client is at risk due to potential inaccuracies in data aggregation. What is the first step the project manager should take to mitigate the risk?
Answer: C
Explanation:
PMI's CPMAI/PMI-CPMAI approach stresses that risk mitigation for data issues starts in the Data Understanding work: identifying appropriate datasets, evaluating training data requirements, and validating data quality/ground truth before proceeding. In practical PMI terms, the project manager should first understand the data characteristics--sources and ownership, schemas, join keys, aggregation logic, definitions, completeness, and known constraints--because aggregation inaccuracies often come from mismatched definitions, inconsistent granularity, duplicate entities, or transformation errors. This aligns with PMI guidance that teams must "identify data needs,"
"locate and characterize data," and then assess quality attributes like accuracy, completeness, and consistency to determine preparation effort and readiness.
NEW QUESTION # 313
During the Data Understanding phase, a project team discovers that only 14 months of transaction history is available for a demand forecasting model that must account for multi-year seasonal cycles.
What is the most appropriate action for the project manager?
Answer: C
Explanation:
CPMAI includes formal go/no-go decision points at the end of each phase. When available data cannot support the defined business objective, the correct response is to return to Business Understanding and reassess feasibility rather than proceeding with a model that cannot meet requirements.
NEW QUESTION # 314
A national health insurance company is embarking on a complex AI project to assist in coordinating patient care across its multiple hospital network. The AI system will analyze large amounts of patient data to coordinate care, improve patient outcomes, and optimize resource allocation. Numerous healthcare providers' data needs to be integrated. The data includes patient information which is private. The project needs to comply with data privacy regulations in various countries. Which critical step should be performed to optimize representative training data?
Answer: D
Explanation:
Improving data understanding and preparation is critical for optimizing representative training data because the team must assess data sources, quality, completeness, diversity, privacy constraints, and integration issues before training the AI system. This helps ensure patient data from multiple providers is usable, compliant, and representative across the hospital network.
NEW QUESTION # 315
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