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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q130-Q135):

NEW QUESTION # 130
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: A

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 # 131
A national health insurance company is embarking on a complex AI project to assist in coordinating patient care across its multiple hospital network. The AI system will analyze large amounts of patient data to coordinate care, improve patient outcomes, and optimize resource allocation. Numerous healthcare providers' data needs to be integrated. The data includes patient information which is private. The project needs to comply with data privacy regulations in various countries. Which critical step should be performed to optimize representative training data?

Answer: B

Explanation:
Improving data understanding and preparation is critical for optimizing representative training data because the team must assess data sources, quality, completeness, diversity, privacy constraints, and integration issues before training the AI system. This helps ensure patient data from multiple providers is usable, compliant, and representative across the hospital network.


NEW QUESTION # 132
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?

Answer: A

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


NEW QUESTION # 133
You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?

Answer: C


NEW QUESTION # 134
You want to create a model to figure out if a customer would be likely to repurchase a certain item. The project owner doesn't want you to create anything too complicated, and you have a limited data set to work with.
Which algorithm is the best choice given these constraints?

Answer: B


NEW QUESTION # 135
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