easyMultiple Choice
MLA-C01 Practice Question: A machine learning engineer at a retail company…
A machine learning engineer at a retail company is monitoring a production model that predicts inventory demand. The model's prediction accuracy has dropped significantly over the past week. The engineer checks the model's input data and notices a new product category was introduced with a different distribution. Which concept is most likely causing the performance degradation?
⚠ Common exam trap
AWS often tests the distinction between covariate shift and concept drift, and the trap here is that candidates confuse a change in input distribution (covariate shift) with a change in the relationship between inputs and outputs (concept drift), leading them to incorrectly select concept drift.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Covariate shift
B is correct because covariate shift occurs when the distribution of the input features changes while the relationship between features and the target remains the same. In this scenario, the introduction of a new product category with a different distribution alters the input data distribution, causing the model to encounter unseen patterns and degrade in prediction accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Concept drift
Why it's wrong here
Concept drift concerns the relationship between inputs and the target changing, whereas the stem describes a new input category with a different distribution — covariate shift. Concept drift would be correct if demand patterns for existing products changed while inputs stayed stable.
- ✓
Covariate shift
Why this is correct
Covariate shift occurs when input feature distributions change between training and production while the underlying relationship remains. The new product category alters the input distribution, so the model encounters patterns unlike its training data, degrading prediction accuracy.
- ✗
Data leakage
Why it's wrong here
Data leakage is contamination during training, such as target information appearing in features, producing inflated offline metrics rather than a live accuracy drop. It would be the answer if the model scored unrealistically well in evaluation but failed immediately in production.
- ✗
Model decay
Why it's wrong here
Model decay describes gradual degradation as the relationship between features and target changes over time, not a sudden shift caused by a new input category. It would fit a model slowly losing accuracy months after training without any identifiable distribution change in incoming data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.