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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation - Address model drift, explainability, and limitations |
| Topic 2: Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production |
| Topic 3: Identify Business Needs and Solutions | 26% | - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions |
| Topic 4: Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact |
| Topic 5: Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Define data requirements and sources - Ensure data quality, privacy, security, and compliance |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
Question #1
An aerospace company is planning to integrate AI for predictive maintenance. The project team needs to identify which parts of the project require cognitive AI solutions. Which method should the project manager perform to meet the objective?
A. Utilizing an AI-driven workflow engine to automate project segmentation
B. Employing a multicriteria decision analysis tool to categorize AI project components
C. Conducting a series of workshops with domain experts to map out AI dependencies
D. Implementing a rule-based system to classify AI project elements based on predefined criteria
Question #2
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Choose all that apply.)
A. Bias-variance tradeoff
B. Hyperparameter settings
C. The algorithm code
D. Supporting training data
Question #3
A transportation company is preparing data for an AI model to optimize fleet management. The project team is working with large amounts of structured and unstructured data. If the project manager avoids addressing the variety of data during preparation, what will be the result?
A. Improved model accuracy
B. Decreased data processing speed
C. Increased data consistency
D. Reduced model performance
Question #4
You are being tasked to manage an AI project at your company and you need to identify which project to start with. What's the best way to approach this?
A. Find a project that requires 100% accuracy in the results and start with that one.
B. Ask key stakeholders from your group and find a small problem that would have a big return on investment and start there.
C. Go through all possible scenarios to come up with the perfect first project.
D. Ask key stakeholders from all groups for input about their pain points.
Question #5
A project manager is overseeing the quality assurance and quality control of an AI/machine learning (ML) model. The model has been trained and initial tests have shown promising results.
However, the project manager is concerned about the long-term performance and reliability of the model in real-world scenarios. What should the project manager do?
A. Set up cross-validation with a larger dataset.
B. Implement additional data augmentation techniques.
C. Establish continuous monitoring and feedback loops.
D. Perform a comprehensive hyperparameter tuning.
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: B,C,D | Question #3 Correct Answer: D | Question #4 Correct Answer: B | Question #5 Correct Answer: C |
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