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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q250-Q255):

NEW QUESTION # 250
You're running an image recognition project and realize that you do not have enough data of a certain type of vehicle. What is the best course of action to get the additional labeled data you need?

A. Perform Data Transformation & Multiplication
B. Perform Data Sampling
C. Perform Data Anonymization
D. Purchase the data from a third party

Answer: A

NEW QUESTION # 251
An aerospace company is integrating AI into their manufacturing process to enhance safety and efficiency. The project team needs to evaluate potential security threats to prevent unauthorized access to sensitive data. What is the highest risk?

A. Secure APIs and data flows by enforcing data governance
B. Employing a proprietary software with no open-source review
C. Implementing an AI model without regular data updates
D. Operationalizing a decentralized data storage system

Answer: D

Explanation:
Operationalizing a decentralized data storage system creates the highest security risk because sensitive manufacturing and AI data may be spread across multiple locations, increasing the attack surface and making access control, monitoring, governance, and protection harder to enforce consistently.

NEW QUESTION # 252
You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?

A. The quality of the data you collect
B. The volume of the data you collect
C. The location/source of your data collection
D. The makeup of your data team

Answer: D

Explanation:
While the data team makeup affects project execution, it has less direct impact on the specific data requirements compared to data quality, volume, and source considerations.

NEW QUESTION # 253
An AI project team needs to consider compliance with data regulations and explainability standards as requirements for a new AI solution. At what point in the project should the requirements be approached?

A. As part of the data preparation phase
B. As part of the final testing phase
C. As part of the business understanding phase
D. As optional guidelines based on project scope

Answer: C

Explanation:
Compliance with data regulations and explainability standards should be addressed during the business understanding phase so they become formal project requirements from the start. This ensures the AI solution is planned, designed, validated, and operationalized in alignment with legal, ethical, and stakeholder expectations.

NEW QUESTION # 254
Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?

A. Projective Analytics
B. Predictive Analytics
C. Diagnostic Analytics
D. Descriptive Analytics

Answer: D

Explanation:
Descriptive analytics focuses on summarizing historical data to identify patterns and performance metrics, providing insights into past behaviors.

NEW QUESTION # 255
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