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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Data Needs | 26% | - Define data requirements and sources - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance |
| Topic 2: Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes |
| Topic 3: Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability |
| Topic 4: Identify Business Needs and Solutions | 26% | - Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy |
| Topic 5: Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks - Manage bias, risk, compliance, and societal impact |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. An AI project team has identified a gap in their data knowledge and experience. They need to address this issue in order to proceed with their AI implementation. What is the effective solution?
A) Utilize an AI-specific data enhancement protocol to improve data quality.
B) Hire an external data consultant to provide targeted guidance and training.
C) Deploy an adaptive data knowledge framework (ADKF) to bridge the expertise gap.
D) Engage in a comprehensive data immersion program to build internal capabilities.
2. Use cognitive technologies/AI when you can't code the rules or you can't scale easily with people or automation. As a good rule of thumb when deciding if AI is right for the project you should:
A) Decide if it's probabilistic or deterministic patterns. If it's deterministic then go with the AI project.
B) Decide if it's a statistics pattern. If it's statistical then go with the AI project.
C) See if simple rules work. If yes, then pick the right AI solution to solve the problem.
D) Decide if it's probabilistic or deterministic patterns. If it's probabilistic then go with the AI project.
3. A consulting firm is determining the feasibility of an AI project. They need to justify the use of AI over noncognitive solutions. The project manager has listed potential noncognitive alternatives.
What is an effective method to support an AI approach?
A) Focusing on the novelty and technological AI appeal
B) Emphasizing the simplicity and reliability of noncognitive solutions
C) Conducting a cost-benefit analysis comparing AI and noncognitive solutions
D) Relying only on industry trends favoring AI adoption
4. Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project. What is the best course of action to take with this data?
A) Careful algorithm selection that reduces the need for data.
B) Data Deduping to reduce overall size and data complexity.
C) Data integration focused on reducing the number of data sources.
D) Data selection and attribute pruning to reduce overall size and data complexity.
5. Your team is trying to determine which pattern best fits their AI problem. To do this the project team is running through the seven patterns of AI to figure out what pattern best applies to their problem. Which of the following is the best approach?
A) Determine what you're trying to accomplish and see which pattern(s) of AI fit best.
B) When in doubt, don't apply any pattern of AI
C) Apply every pattern to the project.
D) When in doubt, go with the patterns and anomalies pattern as all AI projects are about pattern matching.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: A |
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