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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q251-Q256):

NEW QUESTION # 251
Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?

Answer: D


NEW QUESTION # 252
A project manager is considering the feasibility of an AI solution for a project aimed at improving customer service response times. They need to decide whether the project requirements can be solved with existing technologies or whether AI is the best approach. Which principle do the project manager's activities represent?

Answer: A

Explanation:
Evaluating cognitive alternatives means determining whether the project requirements truly need AI-based cognitive capabilities or can be addressed using existing technologies, traditional automation, or non-AI approaches.


NEW QUESTION # 253
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: A

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 # 254
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Choose all that apply.)

Answer: B,C,D

Explanation:
Neural network models require the algorithm code, supporting training data, and hyperparameter settings to fully define the model for training, evaluation, and deployment.


NEW QUESTION # 255
As an organization building an AI solution for your current customers based in NYC, but with possible plans for future expansion, how should you handle worldwide AI laws and regulations?

Answer: D

Explanation:
It is essential to comply with relevant data, privacy, and AI laws not only in the current operating region but also in regions targeted for expansion, ensuring legal readiness and avoiding future compliance risks.


NEW QUESTION # 256
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