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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 different script would likely cause runtime errors, not just inaccurate predictions.
- ✓
The test set is not representative of the production data distribution.
Why this is correct
Correct: Data drift causes model to perform poorly on new data despite good test metrics.
- ✗
The model was trained with a wrong algorithm.
Why it's wrong here
Linear regression is suitable for regression; algorithm choice is not the issue.
- ✗
The model is overfitting the training data.
Why it's wrong here
Overfitting would result in lower test R², not high.
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