DA0-002 Data Analysis Practice Question
A data analyst is preparing to build a predictive model. Which TWO steps are essential to ensure model validity? (Choose two.)
⚠ Common exam trap
Watch out — candidates often think using the entire dataset for training (Option D) is acceptable because it maximizes data for learning, but they overlook the necessity of a separate testing set to validate model performance and avoid overfitting.
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
✓
Perform cross-validation
Cross-validation is essential for model validity because it partitions the data into multiple folds, training on k-1 folds and validating on the remaining fold, which provides a robust estimate of model performance and reduces overfitting. This technique ensures that the model generalizes well to unseen data by repeatedly testing different subsets, making it a standard practice in predictive modeling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase model complexity
Why it's wrong here
Increasing complexity risks overfitting and does not guarantee validity.
- ✓
Perform cross-validation
Why this is correct
Cross-validation provides a more reliable estimate of model performance.
- ✗
Avoid feature selection
Why it's wrong here
Feature selection can improve model interpretability and reduce overfitting.
- ✗
Use the entire dataset for training
Why it's wrong here
This prevents unbiased evaluation of model performance.
- ✓
Split data into training and testing sets
Why this is correct
Essential to evaluate model performance on unseen data.
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Written by Johnson Ajibi, MSc IT Security
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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.