PMI CPMAI : Cognitive Project Management in AI (PMI-CPMAI)

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PMI CPMAI Exam Syllabus Topics:

PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:

1. As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?

A) Develop a unique profile of each individual, and have that profile learn and adapt over time for a wide variety of purposes
B) Develop a unique profile of each type of individual, and have that profile stay the same over the lifetime of that user
C) Develop a unique profile of each individual, and having that profile both learn and adapt over time as well as be programmed for a wide variety of purposes
D) Develop a unique profile of each individual, and manually update that profile over time for a wide variety of purposes

2. A fraud detection system must minimize false positives to avoid incorrectly flagging legitimate transactions. The team is selecting evaluation metrics to optimize model performance accordingly. Which metric should be prioritized in this scenario?

A) Precision
B) F1-score
C) Recall
D) Accuracy

3. 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) Cross-validation
B) Data augmentation
C) Stratified sampling
D) Transfer learning

4. During Model Evaluation, a project team observes that a model performs substantially better on training data than on the holdout test set.
What does this indicate, and what is the appropriate response?

A) Overfitting; apply regularization or simplify the model
B) Class imbalance; apply resampling
C) Underfitting; increase model complexity
D) Data leakage; rebuild the feature set

5. A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data centric projects. What's the next best course of action?

A) Hire an outside consulting firm to handle the technical aspects while you train the team yourself on data centric best practices
B) Get the existing team up to speed and make sure existing Agile approaches can support the AI effort
C) Hire an entirely new team making sure there is at least one data scientist on this centrteam
D) Bring in data centric methodology best practices to get this project back on track

Solutions: