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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
AI Model Development and Iteration		- Model building and validation 1. Iterative delivery approach 2. Machine learning / generative AI model development
AI Operationalization and Governance		- Deployment and lifecycle management 1. AI governance and responsible AI 2. Continuous improvement and monitoring
Data Preparation for AI		- Data cleaning and transformation 1. Feature engineering and preparation 2. Data quality assurance
Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Feasibility and ROI analysis 2. Define AI business objectives
Data for AI		- Data identification and governance 1. Data sourcing and selection 2. Data compliance and ethics
AI System Testing and Evaluation		- Model evaluation and monitoring 1. Performance evaluation and drift detection 2. Explainability and reliability assessment

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q159-Q164):

NEW QUESTION # 159
A government agency plans to increase personalization of their AI public services platform. The agency is concerned that the personal information may be hacked. Which action should occur to achieve the agency's goals?

A. Educate employees on new technologies so they can help users.
B. Develop user-friendly interfaces which are tested by users.
C. Standardize service protocols to deliver services for reliability.
D. Enhance data privacy to increase user trust and confidence.

Answer: D

Explanation:
Enhancing data privacy directly addresses the risk of personal information being compromised while supporting personalization. Strong privacy protections increase user trust and confidence in the AI public services platform.

NEW QUESTION # 160
A project manager is defining the operationalization plan for a deployed model and must specify when retraining occurs.
What is the most effective approach?

A. Retrain when a stakeholder reports dissatisfaction with outputs
B. Define performance thresholds that automatically trigger retraining review
C. Retrain whenever new training data becomes available
D. Retrain on a fixed quarterly schedule

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 # 161
A team preparing data for a churn model transforms raw timestamps into derived attributes such as days since last purchase and purchase frequency over 90 days.
What activity is being performed?

A. Data augmentation
B. Data normalization
C. Feature engineering
D. Dimensionality reduction

Answer: C

Explanation:
Feature engineering creates new input variables from raw data to expose relationships that models can learn more effectively. Derived behavioral attributes often provide substantially more predictive signal than the underlying raw timestamps.

NEW QUESTION # 162
Clean, well-labeled datasets used for machine learning are partitioned into three subsets:
Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?

A. Split by patterned subsampling
B. Split by random subsampling
C. Use the same data for all sets
D. Split by alphabetical order

Answer: B

NEW QUESTION # 163
For AI projects the code and systems don't matter as much as the data. In fact, big data is what's powering much of this latest wave of AI. What's most important for your company to consider around data?

A. Have team members that have experience, understanding of tools, and the ability to deal with massive volumes of data.
B. Collect enormous amounts of data - the more data the better.
C. Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.
D. Understanding which algorithms are best for your data needs.

Answer: A

Explanation:
Having team members with the experience and skills to manage, process, and analyze massive volumes of data is crucial for leveraging big data effectively in AI projects. Proper expertise ensures data is handled correctly to drive meaningful insights and model performance.

NEW QUESTION # 164
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