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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q260-Q265):

NEW QUESTION # 260
Recently your company has been getting a large number of spam emails and some employees have been clicking on these suspicious emails causing a headache for IT. The head of IT wants to create a more robust spam filter and your team has been tasked with this project. What type of algorithm would you select for this problem?

A. Binary (or Binomial) Classification
B. Clustering
C. Multiclass Classification
D. Regression

Answer: A

NEW QUESTION # 261
Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project. What is the best course of action to take with this data?

A. Careful algorithm selection that reduces the need for data.
B. Data selection and attribute pruning to reduce overall size and data complexity.
C. Data integration focused on reducing the number of data sources.
D. Data Deduping to reduce overall size and data complexity.

Answer: B

Explanation:
Data selection and attribute pruning help reduce dataset size and complexity by focusing on relevant features and samples, making model training more efficient without losing important information.

NEW QUESTION # 262
A project manager overseeing an AI project is concerned about potential data privacy violations.
Before progressing further with the project, what should the project manager do to address this concern?

A. Conduct a thorough compliance audit against data regulations.
B. Establish a cross-functional team to handle data governance.
C. Implement real-time monitoring tools for data access control.
D. Develop a comprehensive data risk management framework.

Answer: A

Explanation:
Conducting a thorough compliance audit against applicable data regulations is the appropriate step before progressing because it verifies whether the project's data collection, processing, storage, access, and usage practices meet privacy and regulatory requirements.

NEW QUESTION # 263
A telecommunications company is implementing an AI-driven customer support system. The project manager is responsible for overseeing the data evaluation. They need to ensure that the AI system provides accurate and helpful responses to customer queries. What is an effective method that helps to ensure these objectives are achieved?

A. Conducting quarterly performance reviews using customer satisfaction surveys
B. Relying on periodic training sessions for customer support staff to improve their understanding of the AI system
C. Implementing a static rule-based system alongside the AI system to handle complex customer questions
D. Regularly updating the AI system's knowledge base with the latest information and feedback from customer interactions

Answer: D

Explanation:
Regularly updating the AI system's knowledge base with current information and feedback from customer interactions helps maintain response accuracy, relevance, and usefulness as products, services, policies, and customer needs change.

NEW QUESTION # 264
Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned.
What would likely have helped avoid this conflict?

A. Everyone on the team should understand the differences between automation and autonomous systems.
B. Nothing - running automation projects like autonomous projects is the correct thing to do.
C. Senior management should become involved in the project.
D. Applying a hybrid approach of automation and AI best practices would have achieved better results.

Answer: A

NEW QUESTION # 265
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