The correct answer is to remove the column from the feature set. This is because a unique identifier like CustomerID has zero predictive power for churn; including it forces the model to memorize individual rows rather than learn generalizable patterns, which directly causes overfitting. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept tests your understanding of data preparation best practices and feature selection—a common trap is thinking that more data always helps, but unique identifiers are noise. A reliable memory tip is the “ID Rule”: if a column is a unique identifier, it must be dropped, as it cannot generalize to unseen data.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 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?
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.
Option C is correct because '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.
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.
✗
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Under the hood, algorithms like decision trees and linear models treat each unique ID as a separate entity; for tree-based models, this can lead to splits that isolate single rows, while for linear models, it introduces perfect multicollinearity if encoded. In real-world scenarios, data leakage can also occur if the ID is inadvertently used to index or join data, making the model appear accurate during training but fail in production. A subtle behavior is that some automated feature engineering tools may flag high-cardinality columns as potential features, requiring explicit exclusion.
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.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Remove the column from the feature set. — Option C is correct because '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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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