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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q169-Q174):

NEW QUESTION # 169
Which of the following best describes the technical definition of Machine Learning?

A. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
B. The application of pre-defined rules and algorithms to solve complex problems.
C. An approach to using increasing levels of intelligence to solve greater cognitive needs from unintelligent automation to autonomous business process.
D. The use of computing technology to enable machines to gain cognitive intelligence.

Answer: A

NEW QUESTION # 170
Enhancing and cleaning data is an important action during which phase of CPMAI?

A. Phase I
B. Phase V
C. Phase III
D. Phase VI
E. Phase II
F. Phase IV

Answer: C

NEW QUESTION # 171
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. Feature engineering
C. Dimensionality reduction
D. Data normalization

Answer: B

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 # 172
A financial institution is planning to use AI capabilities to detect fraudulent transactions. The project manager needs to ensure that all necessary requirements are met before proceeding.
What is a necessary initial task?

A. Evaluating the accuracy of current fraud detection methods
B. Identifying the primary stakeholders and their needs
C. Determining the scalability of AI solutions for transaction monitoring
D. Assessing the ethical implications of using AI for fraud detection

Answer: B

Explanation:
Identifying the primary stakeholders and their needs is a necessary initial task because the project manager must understand the business, regulatory, operational, security, and user requirements before determining whether the AI fraud detection solution should proceed.

NEW QUESTION # 173
Your organization wants to use Generative AI. What are examples of when Generative AI can and should be used? (Select all that apply.)

A. Content Generation
B. Virtual Avatars and Characters
C. Explainable Decision-support systems
D. Human Augmentation
E. Programmatic automated content generation
F. Data Augmentation for Training

Answer: A,D,E,F

NEW QUESTION # 174
......

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