Question 840 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is preparing a dataset for a binary classification model to predict customer churn. The dataset contains a timestamp column 'signup_date' that is not relevant for the prediction. What is the most appropriate action to handle this column?
⚠ Common exam trap
AWS often tests the misconception that all timestamp data must be transformed into numeric features, but the key is to first assess relevance—if the column is explicitly not relevant, dropping it is the correct action, not engineering features from it.
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
✓
Drop the 'signup_date' column from the dataset.
The 'signup_date' column is explicitly stated as not relevant for the prediction. In binary classification for customer churn, including an irrelevant timestamp can introduce noise, increase dimensionality, and potentially cause overfitting. Dropping the column is the most appropriate action to maintain model simplicity and focus on predictive 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.
- ✗
Apply one-hot encoding to the year, month, and day components.
Why it's wrong here
This unnecessarily increases dimensionality and does not help churn prediction directly.
- ✗
Convert the timestamp to a numeric feature (e.g., days since signup) and include it.
Why it's wrong here
Converting to numeric may still introduce irrelevant information and overfitting.
- ✗
Use leave-one-out encoding based on the target variable.
Why it's wrong here
Leave-one-out encoding is for categorical features with many levels, not for timestamps.
- ✓
Drop the 'signup_date' column from the dataset.
Why this is correct
Irrelevant columns should be removed to prevent noise.
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Last reviewed: Jun 30, 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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