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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability |
| Topic 2: Identify Business Needs and Solutions | 26% | - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria |
| Topic 3: Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations - Oversee model design, training, and validation |
| Topic 4: Identify Data Needs | 26% | - Define data requirements and sources - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure |
| Topic 5: Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
Question #1
A financial services firm is operationalizing an AI-driven fraud detection system. The project manager needs to ensure the tool complies with relevant data privacy laws while providing secure data access to only authorized personnel. What is an effective technique to address these requirements?
A. Implementing real-time data verification (RTDV) processes
B. Developing a comprehensive data classification policy (DCP)
C. Utilizing role-based access control (RBAC) to limit data access
D. Conducting a privacy impact assessment (PIA) to identify risks
Question #2
Your team is working on a new project for finding the most optimal flow of warehouse robots on the warehouse floor. Which type of machine learning approach would be most appropriate to pick for this problem?
A. All would work equally well
B. Supervised Learning
C. Reinforcement Learning
D. Unsupervised Learning
Question #3
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. Determine and apply data transformation and standardization steps
C. Skip validation to save time
D. Replace real data with only synthetic data
Question #4
Before fully deploying a new recommendation algorithm, the team wants to evaluate its performance against the existing system using real users. The goal is to measure impact on engagement and business metrics. What technique should be used?
A. A/B testing
B. Feature engineering
C. Data cleaning
D. Cross-validation
Question #5
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. Applying past experiences to solve unfamiliar problems.
C. Manipulating the environment through random action.
D. Performing tasks based on symbolic reasoning.
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: C | Question #3 Correct Answer: B | Question #4 Correct Answer: A | Question #5 Correct Answer: B |







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