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

1. A project manager is reviewing the performance of an AI model used for predictive analytics in sales. The model's accuracy is within acceptable limits; however, its precision is low.
What is the cause for the precision issue?

A) The model is underfitting the validation data
B) The training data is unbalanced
C) The feature selection process is flawed
D) The model is overfitting the training data

2. You're working with an inexperienced team and this is all their first AI project. You're trying to work on a supervised learning binary classification problem to determine if emails are spam or not.
What is the best approach for this project?

A) Pick an ensemble method since you're not sure which algorithm will perform best
B) Pick a neural network algorithm since you know this works well for supervised learning approaches
C) Pick a simple algorithm such as Gaussian mixture
D) Pick a simple algorithm such as naive bayes

3. Your team is working on a project and is running into some issues. You need someone on the team who is able to solve problems in environments of uncertainty, can deal with failure, and has the math and data visualization skills needed to communicate the results with others so the issues can get resolved.
What role would be most suited for this?

A) Data Engineer
B) Citizen Data Scientist
C) Project Manager
D) Data Scientist

4. Your model has been working fine for the last three months, however recently you notice the model's performance has greatly declined. What seems to have been overlooked in your workflow pipeline?

A) Model Operationalization
B) Model retraining
C) Model Drift
D) Model reevaluation

5. You have an Anomaly Detection project you're working on and you need a simple approach of clustering data into classified groups. Which algorithm is the best choice given this situation?

A) Hidden Markov Model
B) Decision Tree
C) K-Means Clustering
D) Neural Network

Solutions: