Data Science MCQ Flashcards
7 cards from real Data Science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Data Science MCQ flashcards as text
Which evaluation metric is most appropriate for a regression problem?
Answer: Root Mean Squared Error
RMSE measures the average magnitude of prediction errors for continuous outputs.
What is the curse of dimensionality?
Answer: Data becomes sparse and distances less meaningful as features increase
As dimensions grow, data sparsity increases and distance-based methods degrade.
Which type of join returns only the rows that have matching keys in both tables?
Answer: INNER JOIN
An INNER JOIN returns only rows where the join condition matches in both tables.
What does a high variance inflation factor (VIF) for a feature indicate?
Answer: Strong multicollinearity with other features
A high VIF signals that a predictor is highly correlated with other predictors.
Which technique randomly deactivates neurons during training to reduce overfitting in neural networks?
Answer: Dropout
Dropout randomly zeroes a fraction of neurons each step, preventing co-adaptation.
What is the main goal of feature engineering?
Answer: Creating informative input variables to improve model performance
Feature engineering transforms raw data into features that better expose patterns to models.
Which sampling method ensures each subgroup of a population is proportionally represented?
Answer: Stratified sampling
Stratified sampling draws from each subgroup in proportion to its size in the population.