MCQs

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Overall MCQs for Reference

1. Which term best describes data points absent from a dataset?
   a. Outliers
   b. Noise
   c. Missing values
   d. Redundant data
   Answer: c. Missing valuesgeeksforgeeks​
2. Which symbol is commonly used to express missing values in a dataset?
   a. TRUE
   b. NA
   c. 0
   d. INF
   Answer: b. NAgeeksforgeeks​
3. What problem can occur if rows with missing values are removed from analysis?
   a. Increased accuracy
   b. Reduced sample size
   c. Enhanced bias
   d. No impact
   Answer: b. Reduced sample sizegeeksforgeeks​
4. Which human error can cause missing values?
   a. Incorrect data entry
   b. Overfitting
   c. Feature scaling
   d. Data encryption
   Answer: a. Incorrect data entrygeeksforgeeks​
5. What does MCAR stand for in missing values context?
   a. Mean Calculated And Returned
   b. Missing Cells And Rows
   c. Missing Completely At Random
   d. Machine Calculated At Random
   Answer: c. Missing Completely At Randomgeeksforgeeks​
6. In which scenario is missing data random but related to other variables?
   a. MCAR
   b. MAR
   c. MNAR
   d. RCAR
   Answer: b. MARgeeksforgeeks​
7. Which pandas function identifies missing values in a DataFrame?
   a. isnull()
   b. dropna()
   c. info()
   d. describe()
   Answer: a. isnull()geeksforgeeks​
8. Which method removes rows with missing values from a DataFrame?
   a. dropna()
   b. fillna()
   c. mean()
   d. remove()
   Answer: a. dropna()geeksforgeeks​
9. Which function fills missing values using a specified value or method?
   a. fillna()
   b. dropna()
   c. rename()
   d. append()
   Answer: a. fillna()geeksforgeeks​
10. Blank cells in a CSV dataset typically represent:
    a. Outliers
    b. Noise
    c. Missing values
    d. Errors
    Answer: c. Missing valuesgeeksforgeeks​
11. What is a drawback of removing rows with missing data?
    a. Simple implementation
    b. Data loss
    c. Improved accuracy
    d. Increased memory usage
    Answer: b. Data lossdataaspirant​
12. Mean imputation works best if data is:
    a. Skewed
    b. Normally distributed
    c. Biased
    d. Categorical
    Answer: b. Normally distributeddataaspirant​
13. Which imputation replaces missing values with the median value?
    a. Mode imputation
    b. Mean imputation
    c. Median imputation
    d. Linear interpolation
    Answer: c. Median imputationdataaspirant​
14. What method is recommended for categorical missing values?
    a. Mode imputation
    b. Mean imputation
    c. Median imputation
    d. Forward fill
    Answer: a. Mode imputationdataaspirant​
15. Which interpolation method assumes missing value lies on a straight line between known values?
    a. Polynomial interpolation
    b. Mode interpolation
    c. Linear interpolation
    d. Spline interpolation
    Answer: c. Linear interpolationdataaspirant​
16. Handling missing values by removing the entire record is called:
    a. Case deletion
    b. Imputation
    c. Interpolation
    d. Scaling
    Answer: a. Case deletionalooba​
17. What is the main disadvantage of case deletion when missing values are not random?
    a. Loss of accuracy
    b. Data bias
    c. Increased variance
    d. Extra complexity
    Answer: b. Data biasalooba​
18. Which approach estimates missing values using statistical modeling?
    a. Predictive modeling
    b. Forward fill
    c. Backward fill
    d. Noise filtering
    Answer: a. Predictive modelingalooba​
19. Which is a basic strategy to handle missing values in machine learning?
    a. Increase model complexity
    b. Ignore missing values
    c. Impute missing values
    d. Lower data quality
    Answer: c. Impute missing valuesigmguru​
20. Multiple Imputation by Chained Equations (MICE) is used to:
    a. Remove entire columns
    b. Estimate missing values using relationships
    c. Normalize features
    d. Cluster data
    Answer: b. Estimate missing values using relationshipsexamtopics​
21. When should rows with missing data be removed?
    a. When missing percentage is high
    b. When data is categorical
    c. When missing percentage is low
    d. When model accuracy is not important
    Answer: c. When missing percentage is lowsaidatascience​
22. Which method fills missing values with the most common value of a feature?
    a. Linear interpolation
    b. Mode imputation
    c. Noise filtering
    d. Mean imputation
    Answer: b. Mode imputationdataaspirant​
23. Which library provides missingno for visualizing missing data in Python?
    a. pandas
    b. missingno
    c. matplotlib
    d. scikit-learn
    Answer: b. missingnomachinelearningplus​
24. Which statistical method fills missing values using average value?
    a. Mode imputation
    b. Mean imputation
    c. Median interpolation
    d. Noise filtering
    Answer: b. Mean imputationdataaspirant​
25. Interpolation techniques for missing values include all EXCEPT:
    a. Linear interpolation
    b. Mode imputation
    c. Polynomial interpolation
    d. Spline interpolation
    Answer: b. Mode imputationdataaspirant​
26. Which imputation method works well for skewed data?
    a. Mean imputation
    b. Median imputation
    c. Forward fill
    d. Random sampling
    Answer: b. Median imputationdataaspirant​
27. Which is TRUE regarding the use of fillna(method=’ffill’)?
    a. Replaces missing values with mean
    b. Forward fills missing value with last observed value
    c. Drops missing rows
    d. Uses interpolation
    Answer: b. Forward fills missing value with last observed valuegeeksforgeeks​
28. Which is considered advanced missing value handling?
    a. Mean imputation
    b. Mode imputation
    c. KNN imputation
    d. Removing missing entries
    Answer: c. KNN imputationsaidatascience​
29. What kind of bias can arise if missing values are not handled well?
    a. Information bias
    b. Selection bias
    c. Measurement error
    d. No bias possible
    Answer: b. Selection biasgeeksforgeeks​
30. Which is NOT a type of missing data?
    a. MCAR
    b. MAR
    c. MNAR
    d. LRAR
    Answer: d. LRARgeeksforgeeks​
31. Imputation using regression is suitable for:
    a. Numerical variables
    b. Categorical variables
    c. Both
    d. None
    Answer: a. Numerical variablessaidatascience​
32. When predictors have missing values, which step is needed before model training?
    a. Outlier removal
    b. Imputation
    c. Model selection
    d. Dimensionality reduction
    Answer: b. Imputationplacementpreparation​
33. Fillna(method=’bfill’) does:
    a. Fills backward using next observed value
    b. Fills forward using previous value
    c. Drops missing rows
    d. Normalizes data
    Answer: a. Fills backward using next observed valuegeeksforgeeks​
34. Which is a main role of missing values visualization?
    a. Improving accuracy
    b. Understanding missing data pattern
    c. Feature engineering
    d. Scaling features
    Answer: b. Understanding missing data patterndatacamp​
35. Which is a drawback of manual filling of missing values?
    a. Incorrect data
    b. Redundancy
    c. Complex data
    d. Overfitting
    Answer: a. Incorrect datasanfoundry​
36. Which type of missing value is dependent on the value itself?
    a. MAR
    b. MCAR
    c. MNAR
    d. None
    Answer: c. MNARgeeksforgeeks​
37. What is the recommended tool for handling missing data in scikit-learn?
    a. MinMaxScaler
    b. SimpleImputer
    c. OneHotEncoder
    d. StandardScaler
    Answer: b. SimpleImputerkaggle​
38. Which missing data treatment uses data from other features to estimate?
    a. List-wise deletion
    b. Regression imputation
    c. KNN imputation
    d. Both b and c
    Answer: d. Both b and csaidatascience​
39. Which method should be avoided if missing values are not MCAR?
    a. Mode imputation
    b. Median imputation
    c. Mean imputation
    d. Forward fill
    Answer: c. Mean imputationdataaspirant​
40. What is “imputation”?
    a. Predicting category
    b. Estimating missing value
    c. Removing missing samples
    d. Overfitting model
    Answer: b. Estimating missing valuegeeksforgeeks​
41. What is a simple approach for handling missing data in numerical features?
    a. One-hot encoding
    b. Mean imputation
    c. Feature scaling
    d. Normalization
    Answer: b. Mean imputationdataaspirant​
42. Which is NOT an imputation technique?
    a. Linear regression
    b. Logistic regression
    c. Forward fill
    d. Standardization
    Answer: d. Standardizationdataaspirant​
43. What is a limitation of only dropping records with missing data?
    a. Introduction of bias
    b. Data redundancy
    c. Improved accuracy
    d. Feature noise
    Answer: a. Introduction of biasalooba+1​
44. How are missing values typically dealt in decision trees?
    a. Through separate branches
    b. Ignored completely
    c. Converted to zero
    d. Outlier transformation
    Answer: a. Through separate branchesscribd​
45. Which approach uses multiple models to estimate missing values?
    a. KNN imputation
    b. MICE
    c. Noise filtering
    d. Outlier detection
    Answer: b. MICEexamtopics​
46. Which statement is TRUE about missingness?
    a. Handling missing data is optional
    b. Handling missing data improves predictions
    c. Missing values never affect model performance
    d. Missing values are always random
    Answer: b. Handling missing data improves predictionsalooba​
47. To estimate a missing value based on a polynomial function is called:
    a. Polynomial interpolation
    b. Forward fill
    c. Regression imputation
    d. Min-max scaling
    Answer: a. Polynomial interpolationdataaspirant​
48. Which method is preferable when most missing values are in a column?
    a. Remove column
    b. Mean imputation
    c. Mode imputation
    d. Interpolation
    Answer: a. Remove columnaimcqs​
49. Using the most frequent value for missing categorical data is called:
    a. Mode imputation
    b. Median filling
    c. Random sampling
    d. Linear interpolation
    Answer: a. Mode imputationdataaspirant​
50. Which technique is NOT recommended for time-series missing values?
    a. Forward fill
    b. Backward fill
    c. Random fill
    d. Interpolation
    Answer: c. Random filldataaspirant+1​

1. https://www.geeksforgeeks.org/machine-learning/ml-handling-missing-values/
2. https://dataaspirant.com/handling-missing-values-machine-learning/
3. https://www.alooba.com/skills/concepts/machine-learning/strategies-for-missing-data/
4. https://www.igmguru.com/blog/machine-learning-mcqs
5. https://www.examtopics.com/discussions/microsoft/view/36891-exam-dp-100-topic-2-question-38-discussion/
6. https://saidatascience.com/data-handling-and-preprocessing/
7. https://www.machinelearningplus.com/machine-learning/missing-data-imputation-how-to-handle-missing-values-in-python/
8. https://www.placementpreparation.io/mcq/machine-learning/page/2/
9. https://www.datacamp.com/tutorial/techniques-to-handle-missing-data-values
10. https://www.sanfoundry.com/data-mining-questions-answers-data-cleaning-data-integration/
11. https://www.kaggle.com/questions-and-answers/167627
12. https://www.scribd.com/document/704628424/15-MCQs-ML-DT-Classification
13. https://aimcqs.com/machine-learning
14. https://www.sanfoundry.com/1000-machine-learning-questions-answers/
15. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
16. https://futureskillsacademy.com/blog/top-machine-learning-mcqs-with-answers/
17. https://www.freshersnow.com/machine-learning-mcqs-and-answers-with-explanation/
18. https://www.geeksforgeeks.org/quizzes/machine-learning-quiz-questions-and-answers/
19. https://www.scribd.com/document/460293437/Quiz-5-Handling-Missing-Valuers-in-KNIME-and-Spark-docx
20. https://ceur-ws.org/Vol-3106/Short_5.pdf