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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Manage AI Model Development and Evaluation	16%	- Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation
Topic 2: Operationalize AI Solution	17%	- Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch
Topic 3: Identify Data Needs	26%	- Define data requirements and sources - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure
Topic 4: Identify Business Needs and Solutions	26%	- Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria
Topic 5: Support Responsible and Trustworthy AI Efforts	15%	- Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q218-Q223):

NEW QUESTION # 218
The team is working to build a data preparation pipeline for the conversational chatbot project.
Which phase of CPMAI is this done?

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

Answer: C

NEW QUESTION # 219
You're creating an AI enabled chatbot that is going to access user data. What areas related to data governance do you need to make sure you're addressing? (Choose all that apply.)

A. Data Quality Issues
B. Data Sharing challenges
C. Privacy Risks
D. Data Quantity Issues
E. Security Risks
F. Change Management Issues
G. Business Risks

Answer: A,B,C,E,G

Explanation:
Data governance for an AI chatbot must address data sharing challenges, privacy risks, security risks, data quality issues, and associated business risks to ensure responsible and compliant data use.

NEW QUESTION # 220
A model achieves 97 percent accuracy during validation but performs poorly in production.
Investigation reveals that a feature in the training set was derived from information that becomes available only after the outcome occurs.
What issue does this describe?

A. Data leakage
B. Overfitting
C. Concept drift
D. Sampling bias

Answer: A

Explanation:
Data leakage occurs when the training data contains information that would not be available at prediction time. This inflates validation performance because the model is effectively learning from the answer, and the advantage disappears once the model is deployed.

NEW QUESTION # 221
You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?

A. Governed AI
B. Responsible AI
C. Explainable AI
D. Ethical AI
E. Transparent AI

Answer: E

Explanation:
Transparent AI includes mechanisms that allow users to understand and contest AI decisions, ensuring openness and accountability in the system.

NEW QUESTION # 222
You have just joined a team and they are working on a new project. The project lead isn't sure what type of technology should be used on this project - AI or a traditional software development approach. What is the best way to determine if you have the criteria for a good AI / ML Project?

A. Determine the long-term need for the organization and build the project to that long-term goal.
B. Determine whether the project has a cognitive technology component and meets a short-term need.
C. Evaluate whether the solution can be done with automation.
D. Determine if the project fits within the scope, budget, and timeline set out.

Answer: B

Explanation:
Evaluating whether the project involves cognitive technology and addresses an immediate need helps determine if AI/ML is the appropriate approach over traditional software development.

NEW QUESTION # 223
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