MCQs

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Overall MCQs for Reference

What is data normalization in data science?
a. Removing duplicates
b. Scaling data
c. Encoding text
d. Plotting graphs
Answer: b. Scaling datagyansetu
Which preprocessing technique is primarily used to ensure features contribute equally in analysis?
a. One-hot encoding
b. Normalization
c. Data augmentation
d. Feature selection
Answer: b. Normalizationwayground
What is feature scaling?
a. Feature removal
b. Resizing data
c. Normalizing values
d. Deleting rows
Answer: c. Normalizing valuesgyansetu
Which of these is NOT a normalization method?
a. Min-max normalization
b. Z-score normalization
c. Decimal scaling normalization
d. Data aggregation
Answer: d. Data aggregationwayground
What is the purpose of data normalization?
a. Reduce dataset size
b. Remove duplicates
c. Scale data to a range
d. Convert text to numbers
Answer: c. Scale data to a rangegyansetu
Which normalization technique scales values between 0 and 1?
a. Z-score normalization
b. Min-max normalization
c. Decimal scaling
d. One-hot encoding
Answer: b. Min-max normalizationwayground
What does Z-score normalization transform data into?
a. Binary format
b. Values centered at mean 0, std 1
c. Values between 0–10
d. Values below -1
Answer: b. Values centered at mean 0, std 1sanfoundry+1
Identify a common method for data preprocessing.
a. Data normalization
b. Data storage
c. Data aggregation
d. Data visualization
Answer: a. Data normalizationsanfoundry
Which method helps reduce the impact of outliers?
a. Mean encoding
b. Z-score normalization
c. Label encoding
d. One-hot encoding
Answer: b. Z-score normalizationgyansetu
Which normalization method involves moving the decimal point of attribute values?
a. Min-max normalization
b. Z-score normalization
c. Decimal scaling normalization
d. Label encoding
Answer: c. Decimal scaling normalizationgtu-mcq
The min-max normalization formula uses which two values?
a. Mean & median
b. Minimum & maximum
c. Quantiles & mean
d. Median & mode
Answer: b. Minimum & maximumwayground
What is the risk of NOT normalizing data before using distance-based algorithms?
a. Overfitting
b. Features dominate based on scale
c. Data redundancy
d. Model underfitting
Answer: b. Features dominate based on scalegyansetu
Which method converts text into numerical format but is not normalization?
a. Label encoding
b. Decimal scaling
c. Min-max normalization
d. Z-score normalization
Answer: a. Label encodinggyansetu
Which technique ensures all features contribute equally when calculating distances?
a. Standardization
b. Aggregation
c. Normalization
d. Sampling
Answer: c. Normalizationwayground
What should be performed before applying PCA?
a. Impute missing values
b. Normalize data
c. Aggregate tables
d. Encode text
Answer: b. Normalize datacozmocard
What is the effect of normalization on outliers?
a. Removes them
b. Minimizes their effect
c. Increases their impact
d. Ignores them
Answer: b. Minimizes their effectgyansetu
Which normalization method is most sensitive to outliers?
a. Z-score normalization
b. Min-max normalization
c. Decimal scaling
d. Binning
Answer: b. Min-max normalizationwayground
Normalized data are centered at ______ and have units equal to standard deviations.
a. 0
b. 5
c. 1
d. 10
Answer: a. 0sanfoundry
For which algorithms is normalization particularly critical?
a. Decision trees
b. K-Nearest Neighbors
c. Naïve Bayes
d. Random forest
Answer: b. K-Nearest Neighborsplacementpreparation
What does normalization minimize in a dataset?
a. Redundancy
b. Relevance
c. Accuracy
d. Values
Answer: a. Redundancyincludehelp+1
Which normalization method is appropriate if data has varying scales and extreme values?
a. Min-max normalization
b. Z-score normalization
c. Decimal scaling
d. Mean encoding
Answer: b. Z-score normalizationgyansetu
Which preprocessing step is performed to bring all values within a 0–1 range?
a. Normalization
b. Data integration
c. Transformation
d. Data cleaning
Answer: a. Normalizationwayground
If raw data’s minimum and maximum values are unknown, which normalization is preferred?
a. Decimal scaling
b. Min-max normalization
c. Z-score normalization
d. One-hot encoding
Answer: c. Z-score normalizationwayground
Standardization transforms features to have:
a. Arbitrary mean & std
b. Mean 0, std 1
c. Max value 1
d. Values squared
Answer: b. Mean 0, std 1saidatascience
Which method normalizes by transforming data attributes using a chosen constant j?
a. Decimal scaling
b. Min-max
c. Z-score
d. Standardization
Answer: a. Decimal scalinggtu-mcq+1
Which of the following is not a form of data normalization?
a. Standardization
b. Rescaling
c. Aggregation
d. Decimal scaling
Answer: c. Aggregationsanfoundry
What is the objective of normalization in databases?
a. Minimize redundancy and dependency
b. Increase duplication
c. Simplify queries
d. Maximize retrieval speed
Answer: a. Minimize redundancy and dependencyeasyexamnotes
Why is data normalization essential before ML model training?
a. Speeds up training
b. Ensures fair feature influence
c. Limits accuracy
d. Increases data bias
Answer: b. Ensures fair feature influencegyansetu
Which scenario does NOT require normalization?
a. Linear regression
b. Distance-based clustering
c. Tree-based models
d. SVM
Answer: c. Tree-based modelsplacementpreparation
Normalization deals with:
a. Missing values
b. Feature scaling
c. Data encoding
d. Outlier detection
Answer: b. Feature scalinggyansetu
The process of normalizing data to a common scale is called:
a. Data imputation
b. Data scaling
c. Data mining
d. Data encoding
Answer: b. Data scalingscribd
Which technique is more robust to outliers?
a. Min-max normalization
b. Mean normalization
c. Z-score normalization
d. Decimal scaling
Answer: c. Z-score normalizationgyansetu
What is the range for min-max normalization?
a. 0 to 100
b. -1 to 1
c. 0 to 1
d. -5 to 5
Answer: c. 0 to 1wayground
In normalization, which value does not affect Z-score?
a. Min
b. Mean
c. Standard deviation
d. Actual value
Answer: a. Minsaidatascience
Normalization is a:
a. Data cleaning method
b. Data transformation method
c. Data encoding technique
d. Data imputation method
Answer: b. Data transformation methodsanfoundry
Which of the following formulas is used in min-max normalization?
a. (x – min) / (max – min)
b. (x – mean) / std
c. (max – min) / x
d. (x + min) / (max – min)
Answer: a. (x – min) / (max – min)wayground
When standardizing, what is the result for an original value equal to the mean?
a. 0
b. 1
c. -1
d. Maximum
Answer: a. 0saidatascience
Which Python library is commonly used for normalization?
a. Seaborn
b. NumPy
c. scikit-learn
d. TensorFlow
Answer: c. scikit-learnsaidatascience
A dataset’s features are in widely different ranges. What should be done?
a. Data cleaning
b. Data normalization
c. Label encoding
d. Missing value treatment
Answer: b. Data normalizationplacementpreparation
What does the term ‘feature scaling’ mean?
a. Encoding categorical variables
b. Reducing features
c. Normalizing numeric variables
d. Removing duplicates
Answer: c. Normalizing numeric variablesgyansetu
What is a drawback of min-max normalization?
a. Affected by outliers
b. Ignores minimum value
c. Slow computation
d. Only for categorical data
Answer: a. Affected by outlierswayground
Z-score normalization is also called:
a. Min-max scaling
b. Standardization
c. Binary scaling
d. Robust scaling
Answer: b. Standardizationsaidatascience+1
What is the effect of normalization on categorical variables?
a. No effect
b. Converts to numbers
c. Drops missing data
d. Normalizes scale
Answer: a. No effectgyansetu
Which scenario benefits the most from normalization?
a. Data visualization
b. Distance-based algorithms
c. Data imputation
d. Text encoding
Answer: b. Distance-based algorithmsplacementpreparation
What happens if normalization is not applied on features with different scales in KNN?
a. No change
b. Large scale features dominate
c. Model speeds up
d. Prediction improves
Answer: b. Large scale features dominategyansetu
When can normalization be omitted in data preprocessing?
a. Using neural networks
b. Using decision trees
c. Logistic regression
d. SVM
Answer: b. Using decision treesplacementpreparation
Which function in scikit-learn performs min-max normalization?
a. scale()
b. MinMaxScaler()
c. normalize()
d. fit()
Answer: b. MinMaxScaler()saidatascience
Normalization and standardization differ because:
a. Normalization uses mean, standardization uses range
b. Normalization scales to, standardization scales to mean 0, std 1sanfoundry
c. Standardization encodes text
d. They are the same
Answer: b. Normalization scales to, standardization scales to mean 0, std 1sanfoundry+1
Which method can be used to revert normalized data to its original scale?
a. inverse_transform
b. transform
c. fit
d. encode
Answer: a. inverse_transformsaidatascience
The min-max normalization mapping is linear between:
a. 0 and 10
b. Min and max
c. Mean and std
d. -1 and 1
Answer: b. Min and maxwaygroundn and maxincludehelp

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