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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q253-Q258):

NEW QUESTION # 253
You are working for a large multinational organization and have been assigned to a new project.
For your new ML project you need to make sure you're managing data privacy and security as you're working with sensitive customer data.
What critical security issues do you need to make sure you address (Select all that apply.):

A. Compliance with Data Privacy Laws even if they are out of your physical jurisdiction
B. Securing model data and metadata
C. Securely storing all data collected for training purposes
D. Securing data at rest

Answer: A,B,C,D

NEW QUESTION # 254
A financial services organization deploys an AI credit-scoring model. Post-deployment monitoring reveals systematic underprediction for a protected demographic group, potentially affecting loan approvals. Stakeholders are concerned about fairness and compliance risks. What should the project manager prioritize FIRST?

A. Conduct a bias and fairness audit
B. Replace the algorithm entirely
C. Retrain the model with additional data
D. Pause the system indefinitely

Answer: A

Explanation:
A fairness audit identifies root causes before corrective action. Immediate retraining or replacement without diagnosis may worsen bias or introduce compliance issues.

NEW QUESTION # 255
A healthcare AI model achieves high predictive accuracy but lacks transparency in decision- making. Regulators require clear explanations for each prediction to ensure patient safety and trust. What is the primary risk of deploying this model as-is?

A. Data duplication issues
B. Reduced system speed
C. Regulatory non-compliance
D. Increased operational costs

Answer: C

Explanation:
Lack of explainability may violate healthcare regulations and safety standards.

NEW QUESTION # 256
An aerospace company is evaluating whether their sensor data meets the requirements for an AI- based predictive maintenance system. The project team needs to ensure that the data's accuracy, resolution, and timeliness are adequate to predict equipment failures. Which method addresses the requirements?

A. Evaluating the data schema and integrating additional data sources
B. Performing a data quality assessment focusing on precision and latency
C. Analyzing data completeness and conducting feature engineering
D. Implementing a data governance framework to ensure compliance

Answer: B

Explanation:
Performing a data quality assessment focused on precision and latency directly verifies whether the sensor data is accurate enough, captured at the right resolution, and available quickly enough to support reliable predictive maintenance decisions.

NEW QUESTION # 257
When building your model you need to make sure you're not only checking for performance and making sure the model is giving the expected results. You also need to make sure the model is accomplishing the business objective.
At what phase of CPMAI is this most appropriate to do this?

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

Answer: C

NEW QUESTION # 258
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