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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI System Testing and Evaluation		- Model evaluation and monitoring 1. Performance evaluation and drift detection 2. Explainability and reliability assessment
Topic 2: Data for AI		- Data identification and governance 1. Data sourcing and selection 2. Data compliance and ethics
Topic 3: AI Operationalization and Governance		- Deployment and lifecycle management 1. Continuous improvement and monitoring 2. AI governance and responsible AI
Topic 4: Data Preparation for AI		- Data cleaning and transformation 1. Data quality assurance 2. Feature engineering and preparation
Topic 5: Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Define AI business objectives 2. Feasibility and ROI analysis
Topic 6: AI Model Development and Iteration		- Model building and validation 1. Iterative delivery approach 2. Machine learning / generative AI model development

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q47-Q52):

NEW QUESTION # 47
You are leading a project to develop a new predictive maintenance solution. Together with your project team you determine your data needs, see if you have access to the data, and then begin working on the project. Which phase best describes the work you are performing?

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

Answer: E

Explanation:
Phase II of CPMAI focuses on understanding and preparing data needs, including data acquisition and assessment before model development begins.

NEW QUESTION # 48
A healthcare organization is developing an Al model to predict patient outcomes. The project manager needs to address issues of missing or incorrect data before training the model. What is an effective method to help ensure data quality?

A. Enhancing with data augmentation techniques
B. Implementing an AI data needs assessment
C. Performing systematic data validation
D. Applying predictive data algorithms

Answer: C

Explanation:
Performing systematic data validation helps identify and correct missing, inaccurate, inconsistent, or incomplete patient data before model training, improving the reliability of the data used to predict patient outcomes.

NEW QUESTION # 49
An organization is establishing oversight for multiple AI initiatives and wants consistent review of fairness, transparency, and acceptable use across projects.
What governance mechanism best addresses this need?

A. A project management office with AI project templates
B. An AI ethics or responsible AI review board with defined authority
C. A model risk management policy issued by internal audit
D. A data quality council reporting to the CIO

Answer: B

Explanation:
A responsible AI review board provides cross-project evaluation of ethical, fairness, and acceptable-use questions with defined authority to approve, condition, or halt initiatives. This creates consistency that project-level controls alone cannot achieve.

NEW QUESTION # 50
Your team is working on an AI system to provide a more personalized experience for customers on your website. What should the team do in regard to determining the pattern of AI with regards to the ROI of the project?

A. First identify the AI pattern you want to use and then figure out the ROI
B. First determine the pattern of AI you want to use and then work with stakeholders to come up with ROI
C. First talk to senior managers who set the ROI of the project
D. First identify the objective you're trying to solve or the ROI you desire and then use that to figure out that correct pattern

Answer: D

Explanation:
Defining the project's objective or desired ROI first guides the selection of the appropriate AI pattern, ensuring the solution aligns with business value and goals.

NEW QUESTION # 51
You need to hire a data scientist to join your team. What skill sets should you be looking for when hiring and interviewing this person? (Select all that apply.)

A. Understanding of algorithms
B. Understanding of tools and technologies for manipulating, collecting, and preparing large data sets
C. Automation skills, especially around creating RPA bots
D. Prompt engineering skills
E. Critical thinking skills
F. Strong math skills, especially in calculus and statistics

Answer: A,B,E,F

NEW QUESTION # 52
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