Exam PMI CPMAI Quizzes, CPMAI Test Price

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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q15-Q20):

NEW QUESTION # 15
A project team is preparing to move to the next phase of implementation with an AI system.
However, they are unsure about the system's transparency requirements. Which next step should the project manager prioritize?

Answer: B

Explanation:
Reviewing existing transparency standards and guidelines should be prioritized because the team must first understand the applicable expectations, regulatory requirements, and accepted practices before defining documentation, reporting, or stakeholder processes for the next phase.


NEW QUESTION # 16
A project team plans to integrate a third-party foundation model into a customer-facing solution.
The vendor does not disclose training data sources or update schedules.
What risk should the project manager document as highest priority?

Answer: C

Explanation:
Undisclosed training data prevents verification of licensing, bias, and compliance exposure, while undisclosed update schedules mean model behavior can change without notice. These risks affect the reliability and legal standing of the deployed solution.


NEW QUESTION # 17
When looking to implement AI to help break the Digital Transformation logjam, it's important to:

Answer: C


NEW QUESTION # 18
In an aerospace manufacturing project, engineers are preparing data to train an AI system for predictive maintenance. They need to transform the data from multiple sensors and ensure it is consistent and accurate before building the model. What should the project manager do to handle the inconsistencies?

Answer: C

Explanation:
Identifying and reconciling conflicting data points directly addresses inconsistencies in sensor data before model training. This helps ensure the predictive maintenance model is trained on accurate, consistent, and reliable inputs.


NEW QUESTION # 19
Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned. What would likely have helped avoid this conflict?

Answer: C

Explanation:
Ensuring that all team members understand the fundamental differences between automation and AI systems helps align expectations and avoids conflicts about the appropriate use of AI best practices.


NEW QUESTION # 20
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