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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q250-Q255):

NEW QUESTION # 250
An organization's leadership team is concerned about the ethical implications of operationalizing their AI model. How should the project manager address these concerns in their presentation to the team?

Answer: C

Explanation:
Demonstrating the use of bias detection tools directly addresses ethical concerns by showing how the project team evaluates fairness, identifies potentially discriminatory outcomes, and supports responsible operationalization of the AI model.


NEW QUESTION # 251
A team is getting ready to begin working on a ML project. They need to build a data preparation pipeline and someone on the team suggests they reuse the same pipeline they created for their last project. What's wrong with this suggestion?

Answer: C

Explanation:
Data preparation pipelines must be tailored to the specific pattern and model needs of each project, so reusing pipelines without customization can lead to suboptimal results.


NEW QUESTION # 252
Clean, well-labeled datasets used for machine learning are partitioned into three subsets:
Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?

Answer: D


NEW QUESTION # 253
A healthcare organization plans to implement an AI system to assist with patient diagnosis. The project manager needs to conduct an ethical risk assessment to ensure the AI system does not propagate bias. What is an effective method that meets the project manager's objective?

Answer: A

Explanation:
Conducting bias audits during development directly supports an ethical risk assessment by systematically testing whether the AI system produces biased outcomes before it is deployed.
This helps identify, measure, and address bias risks early in the diagnostic workflow.


NEW QUESTION # 254
An energy technology company is in the data preparation phase of developing an AI model to predict equipment maintenance needs. The project manager is deciding how to handle data from multiple formats. What is an effective technique the project manager should use?

Answer: D

Explanation:
Schema mapping is effective when data comes from multiple formats because it aligns fields, structures, and relationships into a consistent format that the AI model can use during preparation and training.


NEW QUESTION # 255
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