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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Fundamentals	16%	- AI capabilities and limitations - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence
Topic 2: Managing AI	8%	- Managing AI project teams and resources - Stakeholder management - Risk management in AI projects
Topic 3: CPMAI Methodology	41%	- Phase III: Algorithm Selection & Model Training 1. Oversee AI/ML model technique selection 2. Manage model training and configuration - Phase I: Problem Identification 1. Identify business needs and solutions 2. Evaluate initial AI feasibility - Phase V: Operationalization 1. Plan for deployment and integration 2. Define success criteria and monitoring - Phase II: Data Identification & Curation 1. Identify data needs 2. Assess data readiness and quality - 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
Topic 4: Machine Learning	13%	- Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision)
Topic 5: Data for AI	13%	- Data strategy and governance - DataOps concepts - Data preparation and preprocessing
Topic 6: Trustworthy AI	9%	- Ethical considerations and bias - Transparency and explainability - Privacy and security

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

Question #1

A telecommunications company is preparing its data for a new AI-driven customer service chatbot. The project manager needs to ensure the data is precisely formatted and cleaned before moving on to the model-building phase. Which principle does this represent?

A. Conducting AI readiness assessments
B. Defining cognitive pattern recognition
C. Performing data preprocessing tasks
D. Implementing phase-specific data augmentation

Reveal Solution Discussion 0

Correct Answer: C 🗳️