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
Option B (Perform cross-validation) is correct because cross-validation, such as k-fold or stratified k-fold, partitions the data into multiple train/validation folds to estimate how well the model generalizes and to detect overfitting, which is essential for establishing model validity. Option E (Split data into training and testing sets) is correct because holding out an independent test set ensures the model is evaluated on data it has never seen, giving an unbiased estimate of predictive performance and guarding against data leakage. Option A is not correct because increasing model complexity can cause overfitting and does not by itself ensure validity. Option C is not correct because skipping feature selection can introduce irrelevant or noisy variables that degrade model performance. Option D is not correct because training on the entire dataset leaves no independent data for evaluation, making it impossible to assess generalization.
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 model complexity adds parameters that fit noise in the training data, inflating variance and degrading generalisation to unseen records, so it cannot ensure validity. Complexity tuning is genuinely useful during hyperparameter optimisation once a validated split exists, but it addresses fit capacity, not the validity safeguards the scenario requires.
- ✓
Perform cross-validation
Why this is correct
Cross-validation partitions the dataset into complementary training and validation folds, so performance is estimated on data the model has not seen. This directly satisfies the stem's validity requirement by detecting overfitting and yielding a generalisable accuracy estimate rather than an optimistic fit to the training set alone.
- ✗
Avoid feature selection
Why it's wrong here
Skipping feature selection leaves irrelevant and redundant variables in the model, inflating variance and degrading predictive accuracy. It is tempting because feeding all available data avoids missing a signal, and that approach would be defensible only with regularisation or dimensionality-reduction techniques applied afterwards.
- ✗
Use the entire dataset for training
Why it's wrong here
Training on the entire dataset leaves no held-out data, so performance cannot be measured on unseen records and overfitting goes undetected. It is tempting because using every record maximises the information available to the algorithm, and it would be correct only after a separate validation or test split has been reserved.
- ✓
Split data into training and testing sets
Why this is correct
Holding back a testing set lets the model be evaluated on data it never saw during training, exposing overfitting. Without this split, performance metrics reflect memorisation rather than genuine predictive validity, so the analyst cannot trust the model on new records.
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Written by Johnson Ajibi, MSc IT Security
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.