DA0-002 Data Analysis Practice Question
A data analyst is building a predictive model to forecast customer churn. The dataset includes a categorical feature 'payment_method' with categories: credit card, debit card, PayPal, and bank transfer. The analyst decides to use one-hot encoding. After encoding, the analyst notices that the model's performance on the training set is excellent but poor on the test set. Which issue is most likely contributing to this problem?
⚠ Common exam trap
The trap here is assuming that any encoding issue causes overfitting, when in fact the primary driver is often the increased dimensionality relative to sample size.
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
✓
The model is overfitting due to high dimensionality from one-hot encoding combined with a small dataset.
Overfitting is indicated by strong training performance and weak test performance. One-hot encoding expands the feature space, and with a small dataset, this can lead to a model that memorizes training noise. The other options do not directly explain overfitting: multicollinearity affects interpretation, data leakage would inflate both training and test scores, and payment methods are nominal so ordinality is irrelevant. Thus, high dimensionality from encoding is the most plausible cause.
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 one-hot encoding caused data leakage from the test set into the training set.
Why it's wrong here
Data leakage occurs when information from outside the training set is used to create the model. One-hot encoding itself does not cause leakage unless the encoding is fit on the entire dataset before splitting. However, the scenario does not mention that the encoding was fit on the full dataset; even if it were, the symptom of overfitting is not typically caused by leakage from encoding alone. Leakage usually leads to overly optimistic performance on both training and test sets, not just training.
- ✗
The one-hot encoding failed to capture the ordinal relationship among payment methods.
Why it's wrong here
Payment methods are nominal, not ordinal, so there is no inherent order. One-hot encoding is appropriate for nominal data. Failing to capture an ordinal relationship is not an issue because none exists. This would not cause overfitting. The problem is more likely related to model complexity and dataset size, not the encoding's treatment of ordinality.
- ✗
The one-hot encoding introduced multicollinearity among the payment method dummy variables.
Why it's wrong here
One-hot encoding creates dummy variables that are mutually exclusive. Including all dummy variables can introduce perfect multicollinearity (the dummy variable trap), but most modeling libraries handle this by dropping one category or using regularization. However, multicollinearity typically affects interpretability rather than causing overfitting. The scenario describes overfitting (good training, poor test), so multicollinearity is not the primary cause.
- ✓
The model is overfitting due to high dimensionality from one-hot encoding combined with a small dataset.
Why this is correct
One-hot encoding increases dimensionality, especially if the categorical feature has many categories. With a small dataset, this can lead to overfitting because the model may learn noise specific to the training set. The symptom of excellent training performance but poor test performance is classic overfitting. High dimensionality from encoding can exacerbate this, particularly if the model is complex. Thus, this is the most likely issue.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This DA0-002 practice question is part of Courseiva's free CompTIA 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 DA0-002 exam.