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PMI CPMAI Exam Syllabus Topics:

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

Question 1

When building your model, you need to make sure you're not only checking for performance and making sure the model is giving the expected results. You also need to make sure the model is accomplishing the business objective. At what phase of CPMAI is this most appropriate to do this?

A. Phase IV
B. Phase VI
C. Phase III
D. Phase II
E. Phase V
F. Phase I

Question 2

The confusion matrix measures how the algorithm performs for a binary classification activity. As your team is running tests to evaluate model performance, they are seeing the model is incorrectly categorizing flowers as trees. Your model is provided the following:

A. True Positive results
B. True Negative results
C. False Positive results
D. False Negative results

Question 3

An AI project team in the healthcare sector is tasked with developing a predictive model for patient readmissions. They need to gather required data from various sources, including electronic health records (EHR), patient surveys, and clinical notes. The team is evaluating which technique will help to ensure the data is comprehensive and reliable. What is an effective technique the project team should use?

A. Utilizing real-time data integration from EHR systems to ensure data freshness
B. Implementing data augmentation techniques to enhance dataset diversity
C. Using federated learning to train models across decentralized data sources without centralizing data
D. Employing natural language processing (NLP) to extract relevant data from clinical notes

Question 4

A data science team has produced working models through a series of ad hoc notebooks with hard-coded file paths and manually executed preprocessing steps.
What risk does this create for operationalization?

A. The team will be unable to explain model predictions
B. The data will not comply with retention policies
C. The models will not achieve sufficient accuracy
D. Accumulated technical debt prevents reliable, reproducible deployment

Question 5

An aerospace company is in the data preparation phase of an AI project. The project team must verify data quality to make a go/no-go decision for model development. They need to integrate data from several sensors with different sampling rates. What is an effective method that helps to ensure data consistency?

A. Utilizing data interpolation methods
B. Aggregating sensor data
C. Developing a custom data integration framework
D. Applying a real-time data synchronization protocol

Solutions: