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Data Science MCQ With Answers

41 practice questions · Last updated on Jan 04, 2023

Q&A guide Share:

Data science is an interdisciplinary field that applies information from data across various application fields by using scientific methods, procedures, algorithms, and systems to infer knowledge and insights from noisy, structured, and unstructured data.

Most asked questions and answers related to Data Science.

Data Science MCQ

1.What is the difference between supervised and unsupervised learning?

2.What does a p-value represent?

3.What is a Type I error?

4.What does statistical power measure?

5.What does the central limit theorem state?

6.What is the difference between correlation and causation?

7.What is a confounding variable?

8.What is data leakage in a modelling context?

9.Why should preprocessing be fitted inside cross-validation folds?

10.Why is standard k-fold cross-validation wrong for time series?

11.What is the bias-variance trade-off?

12.Which regularisation technique performs automatic feature selection?

13.Why is accuracy misleading on imbalanced data?

14.What does the ROC curve plot?

15.What does R-squared measure in a regression model?

16.What is multicollinearity in a regression model?

17.When should logistic regression be used instead of linear regression?

18.What does a decision tree risk without depth or leaf size constraints?

19.How does Random Forest reduce overfitting compared with a single tree?

20.How does gradient boosting differ from bagging?

21.Which approach usually performs best on tabular business data?

22.What does principal component analysis do?

23.What assumption does k-means clustering make about cluster shape?

24.What is the purpose of feature scaling?

25.What should be established before imputing missing values?

26.How should outliers be handled?

27.What is a baseline model used for?

28.What is the difference between a hyperparameter and a parameter?

29.What does A/B testing establish that observational analysis cannot?

30.Why does peeking at an A/B test and stopping early inflate false positives?

31.What is Simpson's paradox?

32.What is data drift in a production model?

33.Why monitor prediction distributions rather than waiting for accuracy metrics?

34.What is train-serve skew?

35.What do SHAP values provide?

36.When is machine learning the wrong approach?

37.What does the F1 score balance?

38.What does the classification threshold control, and who should set it?

39.What is the purpose of stratified sampling in cross-validation?

40.Why should group-aware splitting be used when multiple rows belong to one entity?

41.What most commonly determines the success of an applied data science project?

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