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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Define data requirements and sources - Ensure data quality, privacy, security, and compliance |
| Topic 2: 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 3: Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact |
| Topic 4: Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability |
| Topic 5: Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
Question #1
Your team is working on an image recognition project, have collected the appropriate data for the project, and have picked a neural network algorithm. They are now ready to train their model. In which phase of CPMAI is this done?
A. Phase IV
B. Phase III
C. Phase I
D. Phase V
E. Phase II
F. Phase VI
Question #2
An image classification project has a limited training set. The team applies rotation, cropping, and brightness variation to existing images to expand the dataset.
What technique is this?
A. Transfer learning
B. Stratified sampling
C. Data augmentation
D. Cross-validation
Question #3
Senior management has tasked your group to analyze a data set to uncover insights into the data. What is the best approach to use to do this?
A. Data Integration
B. Data Governance
C. Data preparation
D. Data Mining or Data Analytics
Question #4
Your team has created a model that is going to be used for monitoring systems and it needs to provide analysis on a weekly basis. What's the most appropriate Model Operationalization approach?
A. Web service / Microservice
B. Batch prediction
C. Stream learning
D. Real-time prediction
Question #5
An IT services company is verifying data quality for an AI project aimed at predicting server downtimes. The project manager needs to decide whether to proceed with data preparation.
Which technique should the project manager use?
A. Exploratory data analysis (EDA)
B. Data augmentation strategies
C. Detailed cost-benefit analysis
D. Advanced data labeling methods
Solutions:
| Question #1 Correct Answer: A | Question #2 Correct Answer: C | Question #3 Correct Answer: D | Question #4 Correct Answer: B | Question #5 Correct Answer: A |


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