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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data for AI | - Data identification and governance
| |
| Topic 2: AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Topic 3: Data Preparation for AI | - Data cleaning and transformation
| |
| Topic 4: AI Model Development and Iteration | - Model building and validation
| |
| Topic 5: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Topic 6: AI Operationalization and Governance | - Deployment and lifecycle management
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. An AI solution performs well in testing but fails after deployment due to differences between training data and real-world production data. The team identifies discrepancies in feature distributions across environments. What is the primary issue affecting performance?
A) Model bias
B) Data drift between environments
C) Hardware constraints
D) Underfitting
2. A retail organization wants to start an AI initiative because a competitor recently announced a machine learning platform. The project manager is asked to launch development immediately using an existing dataset.
What should the project manager do first?
A) Assess the quality and completeness of the existing dataset
B) Assemble a data science team and begin exploratory analysis
C) Define the specific business problem and measurable success criteria
D) Procure a machine learning platform comparable to the competitor's
3. You need to hire a data scientist to join your team. What skill sets should you be looking for when hiring and interviewing this person? (Choose all that apply.)
A) Understanding of tools and technologies for manipulating, collecting, and preparing large data sets
B) Understanding of algorithms
C) Critical thinking skills
D) Prompt engineering skills
E) Strong math skills, especially in calculus and statistics
F) Automation skills, especially around creating RPA bots
4. A healthcare provider is operationalizing an AI tool to assist in diagnostic processes. To ensure robust model governance, they need to address data privacy and ethical considerations. What should the project manager do?
A) Set up a continuous CUE review process.
B) Implement a multi-tiered DCA framework.
C) Develop a detailed privacy impact assessment (PIA).
D) Establish a comprehensive DPMS protocol.
5. A development team is tasked with creating an AI system to assist physicians with diagnosing medical conditions. They encountered cases where symptoms do not always lead to well-defined diagnoses. Which approach should the project manager integrate to handle the inherent uncertainty?
A) Increase the number of input variables.
B) Enhance the knowledge base with more detailed rules.
C) Keep a human in the loop with all decision-making.
D) Implement a more complex retrained model.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: A,B,C,D,E | Question # 4 Answer: C | Question # 5 Answer: C |
