The answer is concept drift. This occurs when the statistical properties of the target variable change over time, so even though the model’s accuracy remains high, it fails to capture new churn patterns because the underlying customer behavior has shifted. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to distinguish concept drift from data drift or overfitting—a common trap is assuming high accuracy means the model is still valid. To detect concept drift in SageMaker, you would use Amazon SageMaker Model Monitor, which compares inference data distributions against a training baseline. Remember the key distinction: data drift is when the input features change, but concept drift is when the relationship between features and the target changes. Memory tip: “Concept drift changes the ‘why’ behind the prediction, not just the ‘what’ in the data.”
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 Amazon SageMaker to deploy a model that predicts customer churn. The model is retrained weekly. The data scientist notices that the model's accuracy remains high, but the business reports that the model is not capturing new churn patterns. 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
✓
The model is experiencing concept drift
Concept drift occurs when the statistical properties of the target variable change over time, causing the model's predictions to become less relevant even if accuracy metrics remain high. In this scenario, the model is retrained weekly but still fails to capture new churn patterns because the underlying customer behavior has shifted—a classic sign of concept drift rather than a data or overfitting issue. Amazon SageMaker's built-in Model Monitor can detect such drift by comparing inference data distributions against a baseline.
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 model is underfitting the data
Why it's wrong here
Underfitting would show low accuracy on both training and test sets.
✗
The model has data leakage from future data
Why it's wrong here
Data leakage would give overly optimistic accuracy, but the model is not capturing new patterns.
✗
The model is overfitting to the training data
Why it's wrong here
Overfitting would cause poor performance on new data, but the accuracy remains high, suggesting the test set may be outdated.
✓
The model is experiencing concept drift
Why this is correct
Concept drift means the underlying data distribution changes, so the model's accuracy on old patterns remains high but it misses new patterns.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates see 'accuracy remains high' and assume the model is overfitting or underfitting, but the key clue is 'not capturing new churn patterns'—which points to a shift in the underlying data distribution (concept drift), not a static model fit issue.
Trap categories for this question
Command / output trap
Underfitting would show low accuracy on both training and test sets.
Detailed technical explanation
How to think about this question
Concept drift can be either sudden (e.g., a new competitor launches) or gradual (e.g., seasonal shifts), and standard retraining on a fixed schedule may not adapt quickly enough if the drift is abrupt. In SageMaker, you can use Amazon SageMaker Clarify or built-in drift detection to monitor feature distributions and prediction quality over time, triggering retraining only when drift exceeds a threshold. A subtle behavior is that accuracy can remain high if the model still predicts the majority class correctly, even as it misses minority churn patterns—this is why precision/recall or business-specific metrics are more informative.
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: The model is experiencing concept drift — Concept drift occurs when the statistical properties of the target variable change over time, causing the model's predictions to become less relevant even if accuracy metrics remain high. In this scenario, the model is retrained weekly but still fails to capture new churn patterns because the underlying customer behavior has shifted—a classic sign of concept drift rather than a data or overfitting issue. Amazon SageMaker's built-in Model Monitor can detect such drift by comparing inference data distributions against a baseline.
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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Question Discussion
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