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PMI CPMAI Exam Syllabus Topics:

Section	Weight	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
Data Preparation for AI		- Data cleaning and transformation 1. Data quality assurance 2. Feature engineering and preparation
Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Feasibility and ROI analysis 2. Define AI business 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. Continuous improvement and monitoring 2. AI governance and responsible AI

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q286-Q291):

NEW QUESTION # 286
A product manager proposes using customer data collected for marketing purposes to train a new recommendation model without updating user consent agreements. The AI team is ready to proceed for faster delivery. What should the project manager do next?

A. Anonymize the data and continue immediately
B. Escalate to legal and compliance teams before proceeding
C. Ignore the issue and proceed
D. Approve the request to meet deadlines

Answer: B

Explanation:
Using data beyond consent violates privacy regulations; legal review is mandatory.

NEW QUESTION # 287
Your team is working on an AI-enabled chatbot to be placed on the website. The goal of the chatbot is to be able to answer questions 24/7 to service clients around the globe. When evaluating your data you realize you don't have enough data to train the model.
What's the best course of action?

A. Use the data that you have and keep the project moving
B. Research what Third-Party Models are available and purchase them to keep the project moving
C. Do not move forward with the project
D. Ask your customer service team to generate additional data for you to use for the project

Answer: B

NEW QUESTION # 288
Major factors for the project you are currently working on is around the training time, cost, and complexity of training your models. Which algorithm is not the best choice given these constraints?

A. Neural Networks
B. Support Vector Machines (SVM)
C. Gaussian Mixture
D. Naive Bayes

Answer: D

NEW QUESTION # 289
A project team at a healthcare provider is determining whether their patient records are adequate for an AI diagnostic tool. They need to validate that the data covers a broad spectrum of conditions and demographics. What is an effective method to assure data suitability?

A. Performing demographic analysis and stratifying patient data
B. Implementing a longitudinal data-gathering approach
C. Conducting a cross-sectional study on data diversity
D. Analyzing data variance and ensuring balanced sampling

Answer: A

Explanation:
Performing demographic analysis and stratifying patient data helps verify that the records adequately represent different patient groups and a broad range of conditions. This supports data suitability by ensuring the diagnostic AI model is trained and evaluated on representative healthcare data.

NEW QUESTION # 290
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?

A. To satisfy stakeholder requests for early delivery
B. To validate that the data pipeline can handle production volumes
C. To reduce the amount of labeled data required
D. To establish a baseline that the AI model must outperform to justify its cost

Answer: D

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 # 291
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