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MLA-C01 Practice Question: A data scientist is using SageMaker to train a…
A data scientist is using SageMaker to train a linear regression model. After training, they evaluate the model on the test set and get an R² of 0.95. However, when they deploy the model to a SageMaker endpoint and run predictions on new data, the predictions are far off. What is the most likely cause?
⚠ Common exam trap
Test-takers frequently confuse high test-set R² with model generalization, overlooking that the test set itself may be non-representative of production data, which is a core concept in the MLA-C01 exam under 'Model Evaluation and Validation'.
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
✓
The test set is not representative of the production data distribution.
A high R² of 0.95 on the test set indicates the model fits the test data well, but if the test set was drawn from the same distribution as the training data and does not reflect the real-world production data, the model will fail to generalize. In SageMaker, the endpoint serves predictions on live data that may have different statistical properties, leading to poor performance despite high test-set metrics. This is a classic case of dataset shift, not a model training or deployment configuration issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The endpoint is using a different inference script.
Why it's wrong here
A mismatched inference script can cause wrong predictions, but the stem gives no evidence of custom inference code. The default SageMaker container reproduces the training transformation; the discrepancy between test-set and endpoint results more likely stems from feature distribution shift or preprocessing mismatch.
- ✓
The test set is not representative of the production data distribution.
Why this is correct
Strong test-set R² with poor endpoint predictions indicates distribution shift: the test set does not represent production data, so offline metrics are misleading. The model itself is sound; the evaluation data differs from live traffic, breaking generalisation.
- ✗
The model was trained with a wrong algorithm.
Why it's wrong here
A wrong algorithm would produce poor test-set metrics, not an R² of 0.95. Algorithm selection is settled during experimentation; the stem's strong offline score with poor live predictions points to a training-serving skew, such as differing feature transformations.
- ✗
The model is overfitting the training data.
Why it's wrong here
Overfitting would depress test-set performance, yet the model scored R² 0.95 on held-out data. Overfitting is the diagnosis when training metrics are strong but validation metrics are weak; here both offline sets agree, so the fault lies in the live data pipeline.
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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.