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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q228-Q233):

NEW QUESTION # 228
Before developing a complex model, a project team builds a simple rules-based version of the solution using existing business logic.
What is the primary purpose of this?

A. To establish a baseline that the AI model must outperform to justify its cost
B. To satisfy stakeholder requests for early delivery
C. To reduce the amount of labeled data required
D. To validate that the data pipeline can handle production volumes

Answer: A

Explanation:
A baseline defines the performance level achievable without machine learning. If a complex model does not meaningfully exceed the baseline, the additional development, infrastructure, and maintenance cost cannot be justified.

NEW QUESTION # 229
During CPMAI Methodology Phase IV: Model Development, which of the following is not done during this phase?

A. Model Selection
B. Algorithm Selection
C. Model training
D. Model tuning

Answer: A

Explanation:
Model selection typically occurs after development, during the evaluation and validation phase, rather than during the Model Development phase where algorithms are selected, trained, and tuned.

NEW QUESTION # 230
You're creating an AI enabled chatbot that is going to access user data. What areas related to data governance do you need to make sure you're addressing? (Choose all that apply.)

A. Data Quality Issues
B. Security Risks
C. Data Quantity Issues
D. Privacy Risks
E. Change Management Issues
F. Data Sharing challenges
G. Business Risks

Answer: A,B,D,F,G

Explanation:
Data governance for an AI chatbot must address data sharing challenges, privacy risks, security risks, data quality issues, and associated business risks to ensure responsible and compliant data use.

NEW QUESTION # 231
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Select all that apply.)

A. The algorithm code
B. Supporting training data
C. Hyperparameter settings
D. Bias-variance tradeoff

Answer: A,B,C

NEW QUESTION # 232
A healthcare project manager is working on implementing an AI-driven diagnostic tool. The project requires identifying key resources, including data scientists, server infrastructure, and external AI consultants. The project manager needs to ensure the allocated resources are optimized for the project's success. Which method will help to ensure the optimal allocation of resources?

A. Applying the theory of constraints to identify and mitigate bottlenecks in resource allocation
B. Implementing a Monte Carlo simulation to predict resource allocation and project timelines
C. Utilizing a resource-leveling technique to balance workloads across all team members
D. Conducting a SWOT (strengths, weaknesses, opportunities, threats) analysis to evaluate project resources

Answer: A

Explanation:
Applying the theory of constraints helps identify the most limiting resource or bottleneck, such as scarce data science capacity, server limitations, or consultant availability. Addressing that constraint first improves overall resource allocation and supports successful AI diagnostic tool implementation.

NEW QUESTION # 233
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