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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: CPMAI Methodology41%- Phase II: Data Identification & Curation
  • 1. Assess data readiness and quality
  • 2. Identify data needs
- Phase IV: Model Evaluation
  • 1. Oversee AI/ML model QA/QC
  • 2. Evaluate model performance against metrics
- Phase V: Operationalization
  • 1. Plan for deployment and integration
  • 2. Define success criteria and monitoring
- Phase I: Problem Identification
  • 1. Identify business needs and solutions
  • 2. Evaluate initial AI feasibility
- Phase III: Algorithm Selection & Model Training
  • 1. Oversee AI/ML model technique selection
  • 2. Manage model training and configuration
- Phase VI: Iteration & Monitoring
  • 1. Retraining and model drift management
  • 2. Continuous monitoring and iteration
Topic 2: Trustworthy AI9%- Transparency and explainability
- Ethical considerations and bias
- Privacy and security
Topic 3: AI Fundamentals16%- AI capabilities and limitations
- Concepts and terminology of Artificial Intelligence
- Types of AI and Machine Learning
Topic 4: Data for AI13%- DataOps concepts
- Data preparation and preprocessing
- Data strategy and governance
Topic 5: Managing AI8%- Stakeholder management
- Risk management in AI projects
- Managing AI project teams and resources
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 (Q80-Q85):

NEW QUESTION # 80
In preparation for an AI solution transition, the project team needs to ensure compliance with data regulations and ethical standards. They must identify and address potential privacy and transparency issues before moving forward.
What is the outcome if these requirements are neglected?

Answer: A

Explanation:
Neglecting privacy, transparency, and regulatory requirements exposes the organization to noncompliance. This can result in legal action, fines, sanctions, and other regulatory penalties during or after the AI solution transition.


NEW QUESTION # 81
A project team plans to integrate a third-party foundation model into a customer-facing solution.
The vendor does not disclose training data sources or update schedules.
What risk should the project manager document as highest priority?

Answer: B

Explanation:
Undisclosed training data prevents verification of licensing, bias, and compliance exposure, while undisclosed update schedules mean model behavior can change without notice. These risks affect the reliability and legal standing of the deployed solution.


NEW QUESTION # 82
Your team is trying to determine which pattern best fits their AI problem. To do this the project team is running through the seven patterns of AI to figure out what pattern best applies to their problem.
Which of the following is the best approach?

Answer: D


NEW QUESTION # 83
When looking to implement AI to help break the Digital Transformation logjam, it's important to:

Answer: D


NEW QUESTION # 84
You're working with a small inexperienced team on a new ML project. Choosing the best algorithm with the best settings given the training and test data is proving to be very hard for them. You lack the critical data science resources available on your team, and can't wait weeks until a data science resource becomes available to join your team. What's your best course of action?

Answer: C

Explanation:
AutoML tools automate algorithm selection and hyperparameter tuning, enabling less experienced teams to develop models efficiently without waiting for specialized data science resources.


NEW QUESTION # 85
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