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PMI CPMAI Exam Syllabus Topics:

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

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

Question 1

Humans have an innate capability that cognitive machines have not been able to replicate. What illustrates the core aspect of human intelligence related to the ability to adapt to new situations?

A. Processing large volumes of information quickly.
B. Performing tasks based on symbolic reasoning.
C. Applying past experiences to solve unfamiliar problems.
D. Manipulating the environment through random action.

Question 2

A healthcare organization is preparing training data for an AI model that predicts patient readmissions. The team discovers inconsistent coding across clinics for the same diagnosis.
Which action best addresses the problem during data preparation?

A. Ignore the inconsistency because the model will learn patterns anyway
B. Replace real data with only synthetic data
C. Determine and apply data transformation and standardization steps
D. Skip validation to save time

Question 3

A project team plans to use customer records containing names, national identification numbers, and health information to train a model. The records were originally collected for billing purposes.
What is the primary concern the project manager must address?

A. Purpose limitation and lawful basis for the secondary use of personal data
B. The dataset may be too small for reliable training
C. Health information may reduce model accuracy
D. The records may require reformatting before ingestion

Question 4

You are working with a dataset that has a high number of dimensions. You're running into issues because some dimensions don't have enough real examples to properly train the systems for predictable results. What's your best course of action?

A. Try to get additional data - at least 5 training examples for each dimension in the representation
B. Try to improve the quality of your data through more preparation
C. Try to get additional information from project lead to see how many examples per dimension are needed
D. Keep going as planned and the problem will eventually correct itself

Question 5

Leadership wants a new HR system built that will better handle potential candidate matching. The project manager assigned to this project believes that the project is well-suited for AI, however they are unsure which pattern of AI this would be. What should the project manager do?

A. Move forward without determining which patter of AI this falls under.
B. Conduct a straw poll with stakeholders to determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
C. Pick an algorithm that seems best suited for the problem and then determine which pattern of AI it is based on the algorithm selected.
D. Determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.

Solutions:

Question 1
Answer: C

Question 2
Answer: C

Question 3
Answer: A

Question 4
Answer: A

Question 5
Answer: D