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PMI CPMAI Exam Syllabus Topics:

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

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

NEW QUESTION # 201
You have been brought on to manage a recognition project, specifically an image recognition project, for an Autonomous Retail application. You know that you need to make sure you have sufficient data for this project.
What's the best way to approach this?

Answer: D


NEW QUESTION # 202
An aerospace company is developing an AI tool for anomaly detection in aircraft engines.
Identifying data sources and their locations is critical. Which method will help to ensure the project meets these objectives?

Answer: B

Explanation:
Establishing a metadata management system helps document where aircraft engine data comes from, where it is stored, what it represents, who owns it, and how it should be used. This supports accurate identification and location tracking of the data sources needed for anomaly detection.


NEW QUESTION # 203
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: B


NEW QUESTION # 204
A project team is evaluating whether an AI initiative should proceed beyond discovery.
Stakeholders are aligned on objectives, but the team has not confirmed data access, quality, or legal constraints.
What is the most appropriate next action?

Answer: C

Explanation:
PMI-CPMAI explicitly includes conducting AI go/no-go assessments as a gated decision mechanism to determine whether conditions are sufficient to proceed. In CPMAI-aligned practice, stakeholder alignment on objectives is necessary but not sufficient; readiness must also cover data availability, permissions, privacy/legal constraints, and the feasibility of meeting acceptable performance metrics. A go/no-go assessment brings these prerequisites into a structured review, allowing the project manager to document assumptions, identify critical gaps (e.g., data rights, retention limits, PII handling), and decide whether to proceed, pivot, or stop before incurring avoidable cost and rework.


NEW QUESTION # 205
Your team is looking to develop an RPA bot to help assist call center agents while on providing support. What type of bot should your team be creating?

Answer: B


NEW QUESTION # 206
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