Question 707 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is preparing a dataset for a machine learning model that predicts customer churn. The dataset contains a column 'CustomerID' that is a unique identifier. What should the data scientist do with this column before training the model?
⚠ Common exam trap
Test-takers frequently think unique identifiers are useful for tracking or that they can be encoded as categorical features, but the exam tests the principle that identifiers with no predictive relationship to the target must be removed to avoid overfitting and data leakage.
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
✓
Remove the column from the feature set.
'CustomerID' is a unique identifier with no predictive power for churn. Including it as a feature would cause the model to memorize individual customers rather than learn generalizable patterns, leading to overfitting and poor performance on unseen data. In machine learning, such columns should be removed during data preparation to ensure the model learns from meaningful features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Keep the column as a feature because it uniquely identifies each customer.
Why it's wrong here
Unique identifiers do not have predictive power and should be excluded.
- ✗
Use the column as the target variable.
Why it's wrong here
The target variable is churn, not CustomerID.
- ✓
Remove the column from the feature set.
Why this is correct
Removing unique identifiers prevents overfitting and is standard practice.
- ✗
Encode the column using one-hot encoding.
Why it's wrong here
One-hot encoding a unique identifier creates many sparse features with no value.
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Last reviewed: Jun 24, 2026
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