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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q68-Q73):

NEW QUESTION # 68
You're working on a computer vision application and realize that you do not have enough real world data for the project. You need additional data created to support your training needs.
Specifically, the images you need are of people in different poses. What is the best way to obtain this data?

Answer: B


NEW QUESTION # 69
Your team is working on a new facial recognition application. Since this technology has the potential to be mis-used you think it's important to set guidelines for the proper use of this application and you want to make sure the AI system is built for some positive purpose. What area of Trustworthy AI does this best fall under?

Answer: D

Explanation:
Responsible AI focuses on ensuring AI systems are developed and used ethically, with proper guidelines and safeguards to promote positive and fair outcomes.


NEW QUESTION # 70
The confusion matrix measures how the algorithm performs for a binary classification activity. As your team is running tests to evaluate model performance, they are seeing the model is incorrectly categorizing flowers as trees.
Your model is provided the following:

Answer: B


NEW QUESTION # 71
Multiple teams are developing AI solutions independently, resulting in inconsistent ethical practices and governance standards. Leadership wants to ensure alignment across all projects.
What is the best action for the project manager to recommend?

Answer: A

Explanation:
Governance frameworks ensure consistent ethical and operational standards.


NEW QUESTION # 72
A project team needs to determine whether to proceed with an AI/machine learning (ML) project by examining various factors. Which factor should be evaluated by the project manager first?

Answer: C

Explanation:
Business objective alignment should be evaluated first because an AI/ML project must address a clearly defined organizational need and deliver measurable business value. Technical decisions such as model selection, data preparation, and optimization should follow only after the project's objectives and expected outcomes are established.


NEW QUESTION # 73
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