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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Machine Learning	13%	- Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning
Trustworthy AI	9%	- Privacy and security - Ethical considerations and bias - Transparency and explainability
Data for AI	13%	- Data strategy and governance - Data preparation and preprocessing - DataOps concepts
Managing AI	8%	- Risk management in AI projects - Managing AI project teams and resources - 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 III: Algorithm Selection & Model Training 1. Manage model training and configuration 2. Oversee AI/ML model technique selection - Phase V: Operationalization 1. Define success criteria and monitoring 2. Plan for deployment and integration - Phase IV: Model Evaluation 1. Oversee AI/ML model QA/QC 2. Evaluate model performance against metrics - 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 I: Problem Identification 1. Evaluate initial AI feasibility 2. Identify business needs and solutions

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

Question #1

An AI project team has identified a gap in their data knowledge and experience. They need to address this issue in order to proceed with their AI implementation. What is the effective solution?

A. Engage in a comprehensive data immersion program to build internal capabilities.
B. Deploy an adaptive data knowledge framework (ADKF) to bridge the expertise gap.
C. Hire an external data consultant to provide targeted guidance and training.
D. Utilize an AI-specific data enhancement protocol to improve data quality.

Reveal Solution Discussion 0

Correct Answer: C 🗳️