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MLS-C01 Modeling Practice Question

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?

⚠ Common exam trap

Many 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.

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

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.

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.

  • 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.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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.