DA0-002 Data Analysis Practice Question
Which TWO of the following are valid techniques for validating the performance of a predictive model?
⚠ Common exam trap
CompTIA often tests the distinction between data preprocessing techniques (like feature scaling) and actual model validation methods, leading candidates to mistakenly select feature scaling as a validation technique because it is a common step in the modeling pipeline.
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
✓
Train-test split
The train-test split (Option C) is a fundamental technique for validating predictive model performance by partitioning the dataset into separate training and testing subsets, ensuring the model is evaluated on unseen data to gauge generalization. This method directly addresses overfitting and provides an unbiased estimate of model accuracy, making it a standard practice in supervised learning workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bootstrapping
Why it's wrong here
Bootstrapping is used for estimating confidence intervals, not typically for model validation.
- ✗
Feature scaling
Why it's wrong here
Feature scaling is a preprocessing step, not a validation technique.
- ✓
Train-test split
Why this is correct
Splitting data into training and testing sets is a basic validation approach.
- ✓
K-fold cross-validation
Why this is correct
K-fold cross-validation partitions data into k folds and iteratively tests on each, providing robust validation.
- ✗
Increasing training data
Why it's wrong here
Increasing data is a method to improve model performance, not to validate it.
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Senior Network & Security Engineer · founder of Courseiva
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.