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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q284-Q289):

NEW QUESTION # 284
Multiple teams are developing AI solutions independently, resulting in inconsistent ethical practices and governance standards. Leadership wants to ensure alignment across all projects.
What is the best action for the project manager to recommend?

A. Establish an AI governance framework and policies
B. Increase model complexity
C. Focus only on technical performance
D. Allow teams full autonomy

Answer: A

Explanation:
Governance frameworks ensure consistent ethical and operational standards.

NEW QUESTION # 285
A company is using an AI model to screen job applications. Over time, the project team notices that the model disproportionately rejects applications from certain educational institutions. What is the first step the project manager should take?

A. Incorporate a fairness constraint into the model's objective function.
B. Retrain the model with equal representation from all institutions.
C. Adjust the model's decision threshold for applications from these institutions.
D. Analyze the historical data for inherent biases against these institutions.

Answer: D

Explanation:
Analyzing the historical data for inherent biases is the first step because the team must identify whether past hiring patterns, labels, or institutional representation caused the model to learn unfair rejection behavior. Understanding the source of bias is necessary before retraining, changing thresholds, or applying fairness controls.

NEW QUESTION # 286
You have been tasked with creating a model that will recommend products based on what other customers have similarly purchased. Which algorithm is the best choice given this situation?

A. K-means
B. Neural Network
C. K Nearest Neighbor
D. Hyperpersonalization

Answer: C

NEW QUESTION # 287
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. Have one team build all models in-house and the other team use pre-trained models and see which team's models perform better.
D. Evaluate your data and use only what you have and build all models in house.

Answer: A

NEW QUESTION # 288
An inexperienced team is training a neural network model on a desktop computer and this is taking a significant amount of time. What would you recommend to them to speed up model training?

A. Break the dataset up into multiple smaller datasets and train the model on each of the smaller datasets over a desktop computer
B. Use a contractor to do the training portion
C. Train the model on GPUs
D. Train the model over multiple desktop computers

Answer: C

NEW QUESTION # 289
......

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