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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q303-Q308):

NEW QUESTION # 303
You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?

Answer: A


NEW QUESTION # 304
A healthcare organization is developing an Al model to predict patient outcomes. The project manager needs to address issues of missing or incorrect data before training the model. What is an effective method to help ensure data quality?

Answer: D

Explanation:
Performing systematic data validation helps identify and correct missing, inaccurate, inconsistent, or incomplete patient data before model training, improving the reliability of the data used to predict patient outcomes.


NEW QUESTION # 305
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: A

Explanation:
Transparent AI includes mechanisms that allow users to understand and contest AI decisions, ensuring openness and accountability in the system.


NEW QUESTION # 306
A healthcare provider had physicians review a potential diagnostic AI application. During their final review, the project team along with the physicians, discovered that the AI model exhibits a higher than acceptable false-positive rate. Before making the go/no-go AI decision, which next step should be performed by the team?

Answer: D

Explanation:
Reevaluating the business objectives and outcomes is necessary before the go/no-go decision because the team must determine whether the false-positive rate is acceptable for the intended diagnostic use case, patient safety expectations, clinical workflow impact, and success criteria.


NEW QUESTION # 307
You're working with an inexperienced team and this is all their first AI project. You're trying to work on a supervised learning binary classification problem to determine if emails are spam or not.
What is the best approach for this project?

Answer: B


NEW QUESTION # 308
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