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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q149-Q154):

NEW QUESTION # 149
Senior management has tasked your group to analyze a data set to uncover insights into the dat

A. What is the best approach to use to do this?
B. Data preparation
C. Data Integration
D. Data Governance
E. Data Mining or Data Analytics

Answer: B

NEW QUESTION # 150
An aerospace company is integrating AI for predictive maintenance. The project manager is concerned about potential delays due to external dependencies. Which initial step should the project manager take?

A. Engage with multiple suppliers
B. Establish contingency plans
C. Implement just-in-time inventory
D. Increase resource allocation

Answer: B

Explanation:
Establishing contingency plans is the appropriate initial step because external dependencies can create schedule risks outside the project team's direct control. Contingency planning helps define fallback actions, alternative timelines, escalation paths, and mitigation strategies before delays affect AI integration work.

NEW QUESTION # 151
A project manager is reviewing the performance of an AI model used for predictive analytics in sales. The model's accuracy is within acceptable limits; however, its precision is low. What is the cause for the precision issue?

A. The training data is unbalanced.
B. The feature selection process is flawed.
C. The model is underfitting the validation data.
D. The model is overfitting the training data.

Answer: A

Explanation:
Unbalanced training data can cause the model to favor the majority class and produce more false positives for the target outcome. This lowers precision because fewer of the model's positive predictions are actually correct.

NEW QUESTION # 152
During CPMAI Phase II, it's important to not only understand the sources of your data but also what data is required for training as well as identifying the features that are required.
When looking to gather data, what approach is best when determining how much data you need?

A. The "more is better" approach
B. The "Goldilocks" approach
C. The "less is better" approach
D. There is no correct approach

Answer: B

NEW QUESTION # 153
During the planning phase, a project manager identifies multiple patterns for their AI project.
Which two actions should the project manager do? (Choose two.)

A. Split the project into iterations for different patterns.
B. Proceed with the most complex pattern to challenge the team.
C. Focus on noncognitive parts before addressing cognitive patterns.
D. Select patterns based on stakeholder preference.
E. Combine all patterns into a single comprehensive iteration.

Answer: A,C

Explanation:
Splitting the project into iterations allows each AI pattern to be planned, tested, and refined separately, reducing complexity and improving control. Addressing noncognitive parts first can establish stable process automation and integration foundations before introducing more complex cognitive AI capabilities.

NEW QUESTION # 154
......

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