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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q26-Q31):

NEW QUESTION # 26
Your team is working on an AI enabled chatbot to be placed on the website. The goal of the chatbot is to be able to answer questions 24/7 to service clients around the globe. When evaluating your data, you realize you don't have enough data to train the model. What's the best course of action?

Answer: B

Explanation:
Leveraging third-party pre-trained models can provide the necessary data and capabilities to progress the chatbot project when in-house data is insufficient.


NEW QUESTION # 27
Your team is ready to operationalize the model they have been working on. It's a model that is meant to be used on an "edge device", specifically a mobile phone and the user may sometimes be in remote locations without regular access to the internet. What's the most important thing to consider here?

Answer: A

Explanation:
For an edge device like a mobile phone used in remote locations with limited internet, the model must live on the device itself to ensure availability and functionality without relying on an internet connection.


NEW QUESTION # 28
A project team is working on an AI initiative that requires ensuring the explainability and transparency of their selected algorithms. They have identified the business requirements and stakeholders. What is an effective way to address the project objectives?

Answer: A

Explanation:
Using a rule-based approach supports explainability and transparency because the decision logic is explicit, traceable, and easier for stakeholders to understand. This makes it suitable when the project requires clear reasoning behind algorithmic outputs.


NEW QUESTION # 29
A financial institution is implementing a new AI system for fraud detection. The project team must ensure the data meets the needs of the AI solution by verifying data quality, completeness, and relevance. They have access to various internal and external data sources. Which method addresses the project team's objectives?

Answer: B


NEW QUESTION # 30
Leadership wants a new HR system built that will better handle potential candidate matching. The project manager assigned to this project believes that the project is well-suited for AI, however they are unsure which pattern of AI this would be.
What should the project manager do?

Answer: A


NEW QUESTION # 31
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