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PMI CPMAI Exam Syllabus Topics:

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

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

1. A machine learning model shows excellent performance on training data but significantly worse results on validation datasets. The team suspects the model memorizes patterns rather than generalizing effectively. Which technique should be applied to address this issue?

A) Increase training epochs indefinitely
B) Use cross-validation techniques
C) Remove validation data
D) Increase model complexity


2. You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing. What type of problem is this?

A) You are overfitting the data
B) You have selected the wrong algorithm
C) You have selected the wrong data
D) You are underfitting the data


3. You recently completed an image recognition project at your company that was focused on identifying different types of cars. You have now been assigned a new image recognition project that is focused on identifying different types of animals. You know you can shortcut model development by using a specific technique. What is this technique called?

A) Reinforcement Learning
B) Generative AI
C) Transfer Learning
D) Pre-Trained Models


4. Your model has been working fine for the last three months, however recently you notice the model's performance has greatly declined. What seems to have been overlooked in your workflow pipeline?

A) Model reevaluation
B) Model Operationalization
C) Model Drift
D) Model retraining


5. A company is evaluating whether to implement AI for a project. They have defined their business objectives and determined the AI capability they want to use. Which action will enable the project manager to move forward with the project?

A) Conducting a go/no-go assessment
B) Identifying the contingency procedures
C) Conducting a data quality assessment
D) Implementing a preliminary version of the AI solution


Solutions:

Question # 1
Answer: B
Question # 2
Answer: A
Question # 3
Answer: C
Question # 4
Answer: D
Question # 5
Answer: A

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