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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch |
| 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: Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks |
| Topic 4: Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance - Define data requirements and sources |
| Topic 5: Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. You're working on a computer vision application and realize that you do not have enough real world data for the project. You need additional data created to support your training needs.
Specifically, the images you need are of people in different poses. What is the best way to obtain this data?
A) Make use of data from different departments
B) Make use of this data from surveillance footage
C) Make use of this data by having employees pose in the positions required
D) Make use of Synthetic Training Data
2. An inexperienced team is training a neural network model on a desktop computer and this is taking a significant amount of time. What would you recommend to them to speed up model training?
A) Train the model on GPUs
B) Break the dataset up into multiple smaller datasets and train the model on each of the smaller datasets over a desktop computer
C) Train the model over multiple desktop computers
D) Use a contractor to do the training portion
3. During development of a healthcare AI diagnostic tool, stakeholders request improving model accuracy by using a complex black-box model, reducing interpretability. Regulatory requirements emphasize transparency in clinical decisions. How should the project manager respond to this request?
A) Replace stakeholders with technical experts
B) Accept the change to improve performance
C) Reject the request immediately
D) Evaluate regulatory and business requirements for explainability
4. A project manager is tasked with ensuring the transparency of an AI project's data selection process. The project involves integrating multiple data sources from various departments. What should the project manager do?
A) Create a comprehensive data catalog.
B) Implement advanced data encryption techniques.
C) Develop a data visualization dashboard.
D) Establish a cross-departmental data sharing agreement.
5. An energy technology company is in the data preparation phase of developing an AI model to predict equipment maintenance needs. The project manager is deciding how to handle data from multiple formats. What is an effective technique the project manager should use?
A) Data warehousing
B) Cross-validation
C) Schema mapping
D) Data consolidation
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: C |
