CompTIA Data+ DA0-002 (V2) Practice Question

A data analyst for an e-commerce company is monitoring the performance of a machine learning model that predicts customer purchasing behavior. For the past six months, the model's accuracy has been stable. However, in the last quarter, its predictive performance has degraded significantly. The analyst investigates and finds that the statistical distribution of incoming data for key features like 'customer age' and 'average session duration' has shifted, no longer matching the data used to train the model. Which of the following data quality assurance concepts BEST describes this issue?

Source control
Requirement testing
Unit test failure
Data drift

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Answer Description

The correct answer is Data drift. Data drift refers to the phenomenon where the statistical properties of the input data for a model change over time, causing the production data to differ from the data the model was trained on. This shift can lead to a degradation in model performance, as seen in the scenario where changes in customer age and session duration distributions are impacting the model's accuracy.

Requirement testing is incorrect because it is the process of validating that a system or its data outputs meet the initial business requirements. This type of testing is typically performed before deployment, not to monitor the performance of a live model in response to changing data patterns.
A unit test is a test that verifies the functionality of a specific, isolated piece of code. A unit test failure would indicate a bug in the code itself, not a change in the statistical properties of the live data flowing through the system. The scenario describes a problem with the data's characteristics, not a software bug.
Source control, also known as version control, is a system for tracking and managing changes to software code, scripts, and other files. It is not a process for monitoring the statistical properties of production data.

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