- A
The endpoint is using a different inference script.
Why wrong: A different script would likely cause runtime errors, not just inaccurate predictions.
- B
The test set is not representative of the production data distribution.
Correct: Data drift causes model to perform poorly on new data despite good test metrics.
- C
The model was trained with a wrong algorithm.
Why wrong: Linear regression is suitable for regression; algorithm choice is not the issue.
- D
The model is overfitting the training data.
Why wrong: Overfitting would result in lower test R², not high.
Question 783 of 1,000
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist is using SageMaker to train a…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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'.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker endpoints run the same model artifact (e.g., model.tar.gz) with the same inference code, so the discrepancy must stem from data distribution differences. In real-world scenarios, production data may suffer from covariate shift (e.g., feature ranges change) or concept drift (e.g., target relationship changes), which R² on a static test set cannot detect. A common mitigation is to monitor endpoint predictions with SageMaker Model Monitor to detect drift and retrain with production data.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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