Practical CPMAI Information | CPMAI Study Plan

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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: CPMAI Methodology	41%	- Phase I: Problem Identification 1. Identify business needs and solutions 2. Evaluate initial AI feasibility - Phase V: Operationalization 1. Plan for deployment and integration 2. Define success criteria and monitoring - Phase III: Algorithm Selection & Model Training 1. Manage model training and configuration 2. Oversee AI/ML model technique selection - Phase IV: Model Evaluation 1. Oversee AI/ML model QA/QC 2. Evaluate model performance against metrics - 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
Topic 2: Data for AI	13%	- Data strategy and governance - DataOps concepts - Data preparation and preprocessing
Topic 3: Managing AI	8%	- Risk management in AI projects - Stakeholder management - Managing AI project teams and resources
Topic 4: AI Fundamentals	16%	- AI capabilities and limitations - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence
Topic 5: Trustworthy AI	9%	- Privacy and security - Ethical considerations and bias - Transparency and explainability
Topic 6: Machine Learning	13%	- Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q64-Q69):

NEW QUESTION # 64
Use cognitive technologies/AI when you can't code the rules or you can't scale easily with people or automation. As a good rule of thumb when deciding if AI is right for the project you should:

A. Decide if it's probabilistic or deterministic patterns. If it's probabilistic then go with the AI project.
B. Decide if it's a statistics pattern. If it's statistical then go with the AI project.
C. See if simple rules work. If yes, then pick the right AI solution to solve the problem.
D. Decide if it's probabilistic or deterministic patterns. If it's deterministic then go with the AI project.

Answer: D

NEW QUESTION # 65
An AI project team is assessing the scalability of a healthcare solution. Which factor should the project manager consider to help ensure the solution is scalable?

A. Compliance with data regulations
B. Ability to handle increased loads
C. Human oversight requirements
D. Integration with the existing infrastructure

Answer: B

Explanation:
The ability to handle increased loads is central to scalability because the healthcare AI solution must continue performing reliably as data volume, users, requests, and operational demand grow.

NEW QUESTION # 66
An AI project team has prepared the data and is ready to proceed with model development.
Which action should the project manager perform next?

A. Ensure go/no-go questions have well-defined answers.
B. Conduct a final assessment of the data quality.
C. Prepare a report on the model's scalability.
D. Document the performance metrics for the model.

Answer: B

Explanation:
Conducting a final assessment of data quality ensures the prepared data is complete, accurate, relevant, consistent, and ready for model development. This helps prevent poor-quality inputs from affecting training results and model reliability.

NEW QUESTION # 67
A logistics company is operationalizing an AI solution to optimize delivery routes. The project manager needs to gather up-to-date information on traffic patterns, delivery schedules, and vehicle performance. Which method will integrate these diverse data types?

A. Building a unified data warehouse
B. Implementing a real-time data processing framework
C. Adopting a federated data model
D. Using an extraction, transformation, and loading (ETL) pipeline

Answer: B

Explanation:
Implementing a real-time data processing framework supports continuous ingestion and integration of current traffic patterns, delivery schedules, and vehicle performance data. This enables the route optimization system to use timely, diverse operational data for accurate decision-making.

NEW QUESTION # 68
In the case that an algorithm you want to use isn't algorithmically explainable, AI systems should try to do the following:

A. Provide a means to interpret AI results so that cause and effect can be represented
B. Provide a means to have a different team on the project
C. Provide a means to have contestability of the algorithm selected
D. Provide a means to reverse-engineer the algorithm to inspect its performance

Answer: A

Explanation:
When algorithms lack inherent explainability, providing interpretable AI results helps users understand cause and effect, improving trust and transparency.

NEW QUESTION # 69
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