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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Managing AI8%- Stakeholder management
- Risk management in AI projects
- Managing AI project teams and resources
Topic 2: Data for AI13%- Data preparation and preprocessing
- DataOps concepts
- Data strategy and governance
Topic 3: CPMAI Methodology41%- Phase VI: Iteration & Monitoring
  • 1. Retraining and model drift management
  • 2. Continuous monitoring and iteration
- Phase IV: Model Evaluation
  • 1. Evaluate model performance against metrics
  • 2. Oversee AI/ML model QA/QC
- 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 II: Data Identification & Curation
  • 1. Assess data readiness and quality
  • 2. Identify data needs
- Phase I: Problem Identification
  • 1. Identify business needs and solutions
  • 2. Evaluate initial AI feasibility
Topic 4: Machine Learning13%- Algorithms and models (e.g., NLP, Computer Vision)
- Supervised, Unsupervised, and Reinforcement Learning
- Deep Learning and Neural Networks
Topic 5: Trustworthy AI9%- Transparency and explainability
- Privacy and security
- Ethical considerations and bias
Topic 6: AI Fundamentals16%- Concepts and terminology of Artificial Intelligence
- AI capabilities and limitations
- Types of AI and Machine Learning

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q208-Q213):

NEW QUESTION # 208
A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data centric projects. What's the next best course of action?

Answer: B

Explanation:
Introducing data-centric methodology best practices directly addresses the core issues in AI projects and helps realign the team's approach for better outcomes.


NEW QUESTION # 209
Your team is working on an AI system to provide a more personalized experience for customers on your website. What should the team do in regard to determining the pattern of AI with regards to the ROI of the project?

Answer: C

Explanation:
Defining the project's objective or desired ROI first guides the selection of the appropriate AI pattern, ensuring the solution aligns with business value and goals.


NEW QUESTION # 210
Your model is going to be used for continuous monitoring of machinery, with need for continuous, instant model predictions. What's the most appropriate Model Operationalization approach?

Answer: D


NEW QUESTION # 211
A project manager is tasked with ensuring the transparency of an AI project's data selection process. The project involves integrating multiple data sources from various departments. What should the project manager do?

Answer: A

Explanation:
Creating a comprehensive data catalog documents available data sources, ownership, definitions, lineage, quality, and selection rationale. This provides transparency into why specific departmental data was chosen and how it will be used in the AI project.


NEW QUESTION # 212
An organization is considering multiple AI solutions to ensure regulatory compliance. What is an effective technique that helps to ensure the AI solution adheres to compliance requirements?

Answer: D

Explanation:
Continuous compliance monitoring provides ongoing oversight of the AI solution against applicable regulatory requirements. It helps identify compliance deviations as they occur and supports timely corrective action as regulations, data, models, and system behavior change.


NEW QUESTION # 213
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