MCQs

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Overall MCQs for Reference

1. What is the main objective of Exploratory Data Analysis (EDA)?
   a. Build predictive models
   b. Summarize data characteristics
   c. Test hypotheses
   d. Store data
   Answer: b. Summarize data characteristicsplacementpreparation​
2. Which technique is commonly used for dimensionality reduction during EDA?
   a. Histogram
   b. Regression
   c. PCA (Principal Component Analysis)
   d. Boxplot
   Answer: c. PCA (Principal Component Analysis)placementpreparation​
3. Why is detecting skewness in data important during EDA?
   a. Improves visualization only
   b. Determines model type
   c. Affects data distribution assumptions
   d. Removes outliers
   Answer: c. Affects data distribution assumptionsplacementpreparation​
4. What plot best represents the relationship between two numerical variables?
   a. Histogram
   b. Boxplot
   c. Scatter plot
   d. Bar chart
   Answer: c. Scatter plotplacementpreparation​
5. Multicollinearity in predictors affects:
   a. Model accuracy positively
   b. Independence among predictors
   c. Removes missing values
   d. Feature selection
   Answer: b. Independence among predictorsplacementpreparation​
6. Which Python library is popular for data visualization like line and bar charts?
   a. NumPy
   b. SciPy
   c. Matplotlib
   d. TensorFlow
   Answer: c. Matplotlibplacementpreparation​
7. Which statistical test is commonly used for comparing means between two groups?
   a. Chi-square test
   b. ANOVA
   c. Student’s t-test
   d. Pearson’s correlation
   Answer: c. Student’s t-testsimplilearn​
8. What is the “law of large numbers” in statistics?
   a. The mean converges to standard deviation
   b. Estimate stabilizes with more samples
   c. Outlier detection
   d. Data normalization
   Answer: b. Estimate stabilizes with more samplessimplilearn​
9. When is one-way ANOVA suitable for analysis?
   a. Comparing categorical variables
   b. Comparing means across multiple groups
   c. Correlation assessment
   d. Dimensionality reduction
   Answer: b. Comparing means across multiple groupssimplilearn​
10. K-means clustering is most suitable for:
    a. Regression problems
    b. Classification problems
    c. Grouping similar data points
    d. Feature selection
    Answer: c. Grouping similar data pointssimplilearn​
11. What term describes an n-dimensional vector of quantitative features in machine learning?
    a. Parameter vector
    b. Weight vector
    c. Feature vector
    d. Decision vector
    Answer: c. Feature vectorsimplilearn​
12. Which distribution is characterized by a bell-shaped curve?
    a. Uniform
    b. Binomial
    c. Normal (Gaussian)
    d. Exponential
    Answer: c. Normal (Gaussian)simplilearn​
13. A confounding variable is:
    a. Unrelated to model outcome
    b. Correlates with both independent and dependent variable
    c. Used in feature scaling
    d. Always categorical
    Answer: b. Correlates with both independent and dependent variablesimplilearn​
14. The drawback of linear regression includes:
    a. Handling non-linear relationships
    b. Dealing with count outcomes or binary outcomes
    c. Solving overfitting automatically
    d. Feature engineering
    Answer: b. Dealing with count outcomes or binary outcomessimplilearn​
15. The “star schema” is used in:
    a. Data clustering
    b. Database design
    c. Feature scaling
    d. Data visualization
    Answer: b. Database designsimplilearn​
16. Why might an algorithm need regular updating?
    a. All data is stationary
    b. To evolve with new data
    c. To remove predictors
    d. For model accuracy alone
    Answer: b. To evolve with new datasimplilearn​
17. Feature engineering primarily involves:
    a. Building neural networks
    b. Creating relevant features for models
    c. Data visualization
    d. Scaling models
    Answer: b. Creating relevant features for modelsscribd​
18. In SQL, which clause is used for aggregating data?
    a. GROUP BY
    b. WHERE
    c. HAVING
    d. ORDER BY
    Answer: a. GROUP BYstratascratch​
19. What is the purpose of cross-validation in model building?
    a. Increase dataset size
    b. Test model performance on independent samples
    c. Normalize data
    d. Remove outliers
    Answer: b. Test model performance on independent samplessimplilearn​
20. When is a confusion matrix used?
    a. Regression analysis
    b. Classification assessment
    c. Data splitting
    d. Missing value imputation
    Answer: b. Classification assessmentsimplilearn​
21. Which of the following is a supervised learning algorithm?
    a. K-means clustering
    b. Linear regression
    c. PCA
    d. t-SNE
    Answer: b. Linear regressioninterviewbit​
22. Deep learning is distinct because it:
    a. Has few layers
    b. Can work with unstructured data and multiple hidden layers
    c. Only uses statistical methods
    d. Relies solely on decision trees
    Answer: b. Can work with unstructured data and multiple hidden layerssimplilearn​
23. Type I error in hypothesis testing means:
    a. Accepting a true null hypothesis
    b. Rejecting a true null hypothesis
    c. Accepting a false null hypothesis
    d. No error
    Answer: b. Rejecting a true null hypothesisitfeature​
24. Which method helps select the best model by penalizing complexity?
    a. R-squared
    b. AIC (Akaike Information Criterion)
    c. Variance Inflation Factor
    d. Chi-Square
    Answer: b. AIC (Akaike Information Criterion)itfeature​
25. What does ROC Curve evaluate?
    a. Regression model performance
    b. Data normalization
    c. Classification model’s ability to discriminate
    d. Feature selection
    Answer: c. Classification model’s ability to discriminatesimplilearn​
26. Time series models handle:
    a. Unordered observations
    b. Categorical variables only
    c. Sequentially ordered data over time
    d. Regression only
    Answer: c. Sequentially ordered data over timesimplilearn​
27. Which algorithm is preferred for anomaly detection in high-dimensional data?
    a. Decision Trees
    b. KNN (K-Nearest Neighbors)
    c. Isolation Forest
    d. Random Sampling
    Answer: c. Isolation Foreststratascratch​
28. “Bagging” in ensemble learning stands for:
    a. Backward regression
    b. Bootstrap Aggregating
    c. Bayesian Aggregation
    d. Block Aggregation
    Answer: b. Bootstrap Aggregatinginterviewkickstart​
29. In statistical inference, the Neyman-Pearson Lemma helps:
    a. Maximize power of a test for given Type I error
    b. Build confusion matrix
    c. Select features
    d. Balance dataset
    Answer: a. Maximize power of a test for given Type I erroritfeature​
30. Which describes “heteroscedasticity” in regression models?
    a. Constant variance in errors
    b. Non-constant variance in errors
    c. No errors
    d. No variance in errors
    Answer: b. Non-constant variance in errorsitfeature​
31. Data wrangling refers to:
    a. Collecting raw data only
    b. Cleaning, transforming, and organizing data
    c. Visualizing data
    d. Building models
    Answer: b. Cleaning, transforming, and organizing datascribd​
32. Power of a test in hypothesis testing means:
    a. Probability of Type I error
    b. Ability to reject a false null hypothesis
    c. Size of sample
    d. Confidence interval
    Answer: b. Ability to reject a false null hypothesisitfeature​
33. Which technique is used for missing value imputation in datasets?
    a. Random sampling
    b. Mean/Median substitution
    c. Data normalization
    d. Feature selection
    Answer: b. Mean/Median substitutionscribd​
34. The purpose of a likelihood ratio test is:
    a. Compare goodness-of-fit between two models
    b. Visualize data
    c. Select features
    d. Build neural networks
    Answer: a. Compare goodness-of-fit between two modelsitfeature​
35. In association rule mining, “support” measures:
    a. Strength of relationship between items
    b. Frequency of itemset occurrence
    c. Probability of error
    d. Model complexity
    Answer: b. Frequency of itemset occurrencesimplilearn​
36. Outlier detection is critical in data analysis because:
    a. Outliers are always errors
    b. Outliers can skew model results
    c. Outliers cannot be handled
    d. Only categorical data have outliers
    Answer: b. Outliers can skew model resultsscribd​
37. The “curse of dimensionality” refers to:
    a. Datasets shrinking as dimensions grow
    b. Data becoming sparse in higher dimensions
    c. No effect of additional variables
    d. Variable selection becomes easier
    Answer: b. Data becoming sparse in higher dimensionsplacementpreparation​
38. In text data analysis, “TF-IDF” stands for:
    a. Time Function of Incremental Data
    b. Term Frequency-Inverse Document Frequency
    c. Term Filtered Data Format
    d. Total Frequency-Incremental Difference
    Answer: b. Term Frequency-Inverse Document Frequencyadaface​
39. In unsupervised learning, which algorithm groups similar items together?
    a. Linear Regression
    b. Decision Trees
    c. K-means
    d. Naive Bayes
    Answer: c. K-meansinterviewbit​
40. In predictive modeling, overfitting means:
    a. Model fits training data well but generalizes poorly
    b. Model fits both training and test data perfectly
    c. Underfitting
    d. Model cannot make predictions
    Answer: a. Model fits training data well but generalizes poorlysimplilearn​
41. Which metric is best to evaluate regression model performance?
    a. Precision
    b. Recall
    c. Mean Squared Error (MSE)
    d. ROC-AUC
    Answer: c. Mean Squared Error (MSE)simplilearn​
42. Principal Component Analysis (PCA) transforms variables to:
    a. Most correlated axes
    b. Least variance axes
    c. Uncorrelated principal axes
    d. Higher dimensional space
    Answer: c. Uncorrelated principal axesplacementpreparation​
43. What is the primary source of “primary data”?
    a. Published research
    b. Surveys or experiments by researcher
    c. Government reports
    d. Review articles
    Answer: b. Surveys or experiments by researchertestbook​
44. A “time delta” in data analysis is:
    a. Total number of variables
    b. Difference between two time points
    c. Count of outliers
    d. Null values in dataset
    Answer: b. Difference between two time pointsplacementpreparation​
45. What statistical measure describes data spread?
    a. Mean
    b. Mode
    c. Standard deviation
    d. Median
    Answer: c. Standard deviationsimplilearn​
46. In context of databases, ETL stands for:
    a. Evaluate, Test, Load
    b. Extract, Transform, Load
    c. Enter, Transmit, Learn
    d. Estimate, Tabulate, List
    Answer: b. Extract, Transform, Loadk21academy​
47. What is a “hypothesis” in statistical analysis?
    a. Guess about data
    b. Statement to be tested statistically
    c. Feature selection decision
    d. Outlier detection method
    Answer: b. Statement to be tested statisticallyitfeature​
48. “Bayesian inference” involves:
    a. Frequentist probability
    b. Updating beliefs with new evidence
    c. Only historical analysis
    d. Clustering algorithms
    Answer: b. Updating beliefs with new evidenceitfeature​
49. “Ensemble methods” improve model accuracy by:
    a. Using single model repeatedly
    b. Combining predictions of multiple models
    c. Removing features
    d. Splitting dataset
    Answer: b. Combining predictions of multiple modelsinterviewkickstart​
50. “Data normalization” in preprocessing ensures:
    a. Features have consistent scale
    b. Models converge slowly
    c. Outliers are removed
    d. Classes are balanced always
    Answer: a. Features have consistent scaleadaface​

1. https://www.placementpreparation.io/mcq/data-science/page/2/
2. https://www.simplilearn.com/tutorials/data-science-tutorial/data-science-interview-questions
3. https://www.scribd.com/document/705848941/Data-Science-MCQ-Questions-and-Answer-PDF
4. https://www.stratascratch.com/blog/advanced-data-analytics-interview-questions/
5. https://www.interviewbit.com/data-science-mcq/
6. https://itfeature.com
7. https://interviewkickstart.com/blogs/articles/data-science-mcq-answers
8. https://www.adaface.com/blog/data-analysis-interview-questions/
9. https://testbook.com/objective-questions/mcq-on-sources-of-data–5eea6a0d39140f30f369e1c2
10. https://k21academy.com/microsoft-azure/data-engineer/70-data-analyst-interview-questions/
11. https://www.sanfoundry.com/1000-data-science-questions-answers/
12. https://testbook.com/objective-questions/mcq-on-data-analysis–5eea6a0d39140f30f369e1bb
13. https://www.gyansetu.in/blog/data-analytics-mcq-quiz-interview-preparation/
14. https://www.geeksforgeeks.org/data-science/data-science-interview-questions-and-answers/