The answer is concept drift in the underlying data distribution. This is correct because concept drift occurs when the statistical properties of the target variable—here, customer churn—change over time due to shifts in real-world behavior, causing a model trained on historical data to fail on new data despite high test accuracy. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to distinguish concept drift from data drift or model overfitting; a common trap is assuming retraining on the same dataset will fix it, but only continuous monitoring and retraining on fresh data address the root cause. Remember the mnemonic “Concept = Change in the Concept to predict” to recall that the relationship between inputs and the target has shifted, not just the input values themselves.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 company uses SageMaker to deploy a model for predicting customer churn. The model was trained on historical data and achieves 85% accuracy on the test set. After deployment, the model's predictions are significantly worse on new data due to changes in customer behavior. 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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Concept drift in the underlying data distribution
The model's performance degradation on new data, despite high accuracy on the test set, is a classic symptom of concept drift. Concept drift occurs when the statistical properties of the target variable (customer churn) change over time due to shifts in customer behavior, making the trained model's decision boundary obsolete. SageMaker deployed the model as a persistent endpoint, but the underlying data distribution has evolved, so the model no longer generalizes to the current environment.
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.
✗
Data leakage during training
Why it's wrong here
Data leakage would inflate training performance, but test performance would also be affected.
✗
The training dataset was too small
Why it's wrong here
Insufficient data would likely cause poor performance from the beginning.
✓
Concept drift in the underlying data distribution
Why this is correct
Changes in customer behavior cause concept drift, reducing model accuracy over time.
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 is overfitting to the training data
Why it's wrong here
Overfitting would result in poor performance on test set as well, not just new data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse concept drift with overfitting, assuming any performance drop after deployment must be due to the model memorizing noise, but the key differentiator is the temporal nature of the degradation tied to changing customer behavior, not a static training-data issue.
Detailed technical explanation
How to think about this question
Concept drift can be either sudden (e.g., a new competitor changes churn patterns) or gradual (e.g., seasonal shifts). In SageMaker, you can monitor for drift using Amazon SageMaker Model Monitor, which tracks prediction distributions and feature attribution over time, triggering retraining when drift is detected. A common subtlety is that the model may still show high accuracy on a static holdout set while failing in production because the holdout set no longer represents the live data distribution.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Concept drift in the underlying data distribution — The model's performance degradation on new data, despite high accuracy on the test set, is a classic symptom of concept drift. Concept drift occurs when the statistical properties of the target variable (customer churn) change over time due to shifts in customer behavior, making the trained model's decision boundary obsolete. SageMaker deployed the model as a persistent endpoint, but the underlying data distribution has evolved, so the model no longer generalizes to the current environment.
What should I do if I get this MLS-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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This MLS-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 MLS-C01 exam.
Question Discussion
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