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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Trustworthy AI	9%	- Transparency and explainability - Ethical considerations and bias - Privacy and security
Managing AI	8%	- Managing AI project teams and resources - Risk management in AI projects - Stakeholder management
Data for AI	13%	- DataOps concepts - Data strategy and governance - Data preparation and preprocessing
CPMAI Methodology	41%	- Phase V: Operationalization 1. Plan for deployment and integration 2. Define success criteria and monitoring - Phase I: Problem Identification 1. Identify business needs and solutions 2. Evaluate initial AI feasibility - Phase IV: Model Evaluation 1. Evaluate model performance against metrics 2. Oversee AI/ML model QA/QC - Phase VI: Iteration & Monitoring 1. Continuous monitoring and iteration 2. Retraining and model drift management - Phase II: Data Identification & Curation 1. Identify data needs 2. Assess data readiness and quality - Phase III: Algorithm Selection & Model Training 1. Manage model training and configuration 2. Oversee AI/ML model technique selection
AI Fundamentals	16%	- Concepts and terminology of Artificial Intelligence - AI capabilities and limitations - Types of AI and Machine Learning
Machine Learning	13%	- Algorithms and models (e.g., NLP, Computer Vision) - Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q111-Q116):

NEW QUESTION # 111
Your team is testing the NLP model they just created to make sure it's performing as expected.
Some of your team members want to move this model to production and move to the next iteration. What's wrong with this workflow?

A. Model Evaluation requires continuous model evaluation, retraining, and operationalization
B. Nothing is wrong with this workflow. You can move to the next iteration
C. Team members should not be able to move to new projects until senior management signs off
D. You need to make sure the AI Go/No Go questions have been addressed

Answer: A

Explanation:
Model evaluation is an ongoing process that requires continuous monitoring, retraining, and re- deployment to ensure the model maintains performance and adapts to new data before fully moving to production.

NEW QUESTION # 112
A manufacturing company is operationalizing an AI-driven quality control system. The project manager needs to ensure data privacy and regulatory compliance due to the critical nature in protecting sensitive operational data. What is an effective technique that addresses these requirements?

A. Using a hybrid encryption scheme for storage
B. Applying data anonymization to the dataset
C. Utilizing a secure multiparty computation framework
D. Implementing a zero-trust architecture for network security

Answer: B

Explanation:
Applying data anonymization protects sensitive operational data by removing or masking identifying information before it is used in the AI quality control system. This supports data privacy and helps meet regulatory compliance requirements while still allowing the dataset to be analyzed.

NEW QUESTION # 113
A project manager is overseeing the development of an AI system and is concerned about maintaining user privacy. The project involves collecting sensitive user data with potential legal and compliance issues to consider.
What should the project manager prioritize to help ensure the AI system adheres to privacy requirements?

A. Establishing robust data encryption protocol for privacy
B. Implementing a minimalistic data collection policy
C. Developing a comprehensive data anonymization strategy
D. Ensuring regular impact assessments are conducted to meet privacy laws

Answer: D

Explanation:
Regular privacy impact assessments help identify how sensitive data is collected, processed, stored, and used, while evaluating associated privacy risks against applicable legal and regulatory requirements. This enables the project team to maintain ongoing compliance throughout the AI system's lifecycle.

NEW QUESTION # 114
During CPMAI Phase II of your project, your team is going through their data collection needs.
One team member wants to make use of pre-trained models while another member is adamantly against it.
As the project lead, what should you do?

A. Evaluate your data and see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.
B. Evaluate your data and see if using pre-trained models make sense. If so, have the team see what pre-trained models your company already owns and use those.
C. Evaluate your data and use only what you have and build all models in house.
D. Have one team build all models in-house and the other team use pre-trained models and see which team's models perform better.

Answer: A

NEW QUESTION # 115
Midway through an iteration, business stakeholders request that the model also predict two additional outcomes not included in the original scope.
What should the project manager do?

A. Evaluate the request against the iteration objective and defer it to a subsequent iteration if it does not fit
B. Extend the current iteration timeline to accommodate the additional outcomes
C. Reject the request as outside the approved scope
D. Incorporate the request to maintain stakeholder engagement

Answer: A

Explanation:
Iterative delivery depends on protecting the current iteration's defined objective while capturing new requirements for future planning. Deferring the request preserves delivery cadence without discarding potentially valuable stakeholder input.

NEW QUESTION # 116
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