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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Fundamentals16%- Concepts and terminology of Artificial Intelligence
- AI capabilities and limitations
- Types of AI and Machine Learning
Topic 2: Machine Learning13%- Supervised, Unsupervised, and Reinforcement Learning
- Deep Learning and Neural Networks
- Algorithms and models (e.g., NLP, Computer Vision)
Topic 3: Data for AI13%- DataOps concepts
- Data strategy and governance
- Data preparation and preprocessing
Topic 4: Trustworthy AI9%- Ethical considerations and bias
- Transparency and explainability
- Privacy and security
Topic 5: CPMAI Methodology41%- Phase V: Operationalization
  • 1. Define success criteria and monitoring
  • 2. Plan for deployment and integration
- Phase IV: Model Evaluation
  • 1. Oversee AI/ML model QA/QC
  • 2. Evaluate model performance against metrics
- 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 II: Data Identification & Curation
  • 1. Identify data needs
  • 2. Assess data readiness and quality
- Phase III: Algorithm Selection & Model Training
  • 1. Manage model training and configuration
  • 2. Oversee AI/ML model technique selection
Topic 6: Managing AI8%- Stakeholder management
- Risk management in AI projects
- Managing AI project teams and resources

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q201-Q206):

NEW QUESTION # 201
Your model is going to be used for continuous monitoring of machinery, with need for continuous, instant model predictions. What's the most appropriate Model Operationalization approach?

Answer: D

Explanation:
Real-time prediction enables continuous, instant model inference required for monitoring machinery, providing timely alerts and decision support without delay.


NEW QUESTION # 202
A consulting firm is preparing data for an AI-driven customer segmentation model. They need to verify data quality before data preparation. What should the project manager do first?

Answer: D

Explanation:
Assessing data completeness should be done first because the team must verify whether the customer data contains the required fields, records, and coverage before cleaning, enhancing, labeling, or preparing it for the segmentation model.


NEW QUESTION # 203
A consulting firm is determining the feasibility of an AI project. They need to justify the use of AI over noncognitive solutions. The project manager has listed potential noncognitive alternatives.
What is an effective method to support an AI approach?

Answer: C

Explanation:
Conducting a cost-benefit analysis comparing AI and noncognitive solutions helps determine whether AI provides enough added value, such as better prediction, automation, scalability, or decision support, to justify its complexity, cost, and implementation effort.


NEW QUESTION # 204
During Model Evaluation, a project team observes that a model performs substantially better on training data than on the holdout test set.
What does this indicate, and what is the appropriate response?

Answer: D

Explanation:
A large gap between training and test performance indicates the model has learned noise specific to the training data rather than generalizable patterns. Regularization, reducing model complexity, or increasing training data volume are standard mitigations.


NEW QUESTION # 205
During CPMAI Phase II it's important to not only understand the sources of your data, but also what data is required for training as well as identifying the features that are required. When looking to gather data, what approach is best when determining how much data you need?

Answer: C

Explanation:
The "goldilocks" approach focuses on gathering an optimal amount of data - enough to train effective models without unnecessary excess - balancing quality and quantity for best results.


NEW QUESTION # 206
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