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PMI CPMAI Exam Syllabus Topics:

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

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CPMAI Real Exam Questions - CPMAI New Question

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q47-Q52):

NEW QUESTION # 47
You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?

Answer: D


NEW QUESTION # 48
A model achieves 97 percent accuracy during validation but performs poorly in production.
Investigation reveals that a feature in the training set was derived from information that becomes available only after the outcome occurs.
What issue does this describe?

Answer: C

Explanation:
Data leakage occurs when the training data contains information that would not be available at prediction time. This inflates validation performance because the model is effectively learning from the answer, and the advantage disappears once the model is deployed.


NEW QUESTION # 49
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 # 50
A project manager is preparing a contingency plan for an AI-driven customer service platform.
They need to determine an effective strategy to handle potential system downtimes. Which strategy addresses the project manager's objective?

Answer: B

Explanation:
PMI-CPMAI explicitly includes "Oversee AI solution contingency plan," with enablers such as incident response procedures, backup and disaster recovery strategies, escalation procedures, and business continuity plans for AI service disruptions, plus regular testing and validation of contingency procedures. For a customer service platform, the most direct "business continuity" control during downtime is a manual override path for critical queries--ensuring essential customer needs can still be handled when automation is unavailable. This strategy creates an immediate operational alternative that keeps service running, aligns with escalation procedures, and reduces harm to customers and the business during outages.


NEW QUESTION # 51
An AI project team is in the process of designing a security plan. The team needs to consider various aspects such as transparency, explainability, and compliance with data regulations.
Which action should the project manager take?

Answer: C

Explanation:
Ensuring the AI system's decisions are transparent and explainable supports a complete security and governance plan by addressing accountability, auditability, regulatory compliance, and stakeholder trust in how the AI solution handles data and produces outcomes.


NEW QUESTION # 52
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