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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q84-Q89):

NEW QUESTION # 84
A company is developing a chatbot that can address user queries 24/7 without human intervention. Which AI capability should be implemented?

Answer: D

Explanation:
Conversational AI enables a chatbot to understand user inputs, interpret intent, generate appropriate responses, and maintain interactions continuously without requiring human intervention.


NEW QUESTION # 85
During which phase of an AI project should you consider Trustworthy AI considerations?

Answer: B


NEW QUESTION # 86
Your organization has just rolled out a new image recognition system and is asking all employees to use it. It was trained using images from the ImageNet test set. After a few weeks, users are finding the results are not as expected and are asking for visibility into all the aspects of what went into building an AI system. What area of Trustworthy AI is being addressed here?

Answer: A

Explanation:
Transparent AI focuses on providing visibility into how AI systems are built, including data sources and processes, to build user trust and understanding.


NEW QUESTION # 87
An AI recommendation system has been in production for four months. The project manager wants to ensure the solution continues to deliver business value.
What is the most effective mechanism?

Answer: C

Explanation:
A feedback loop captures actual outcomes from production use and returns them to the monitoring and retraining process. This closes the CPMAI lifecycle, allowing the solution to be measured against business results and improved through subsequent iterations.


NEW QUESTION # 88
You recently completed an image recognition project at your company that was focused on identifying different types of cars. You have now been assigned a new image recognition project that is focused on identifying different types of animals. You know you can shortcut model development by using a specific technique. What is this technique called?

Answer: C

Explanation:
Transfer Learning involves leveraging a model trained on one task (e.g., car recognition) and adapting it to a related task (e.g., animal recognition), significantly reducing development time and resources.


NEW QUESTION # 89
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