MCQs

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Overall MCQs for Reference

Which of the following is the primary purpose of data preprocessing in data science?
a. Data collection
b. Data visualization
c. Preparing raw data for analysis
d. Data storage
Answer: c. Preparing raw data for analysissanfoundry+1
Which data preprocessing technique is used to scale features to a uniform range?
a. Data reduction
b. Data normalization
c. Data integration
d. Data visualization
Answer: b. Data normalizationalooba+1
What is the process of combining data from multiple sources called?
a. Data cleaning
b. Data transformation
c. Data integration
d. Data reduction
Answer: c. Data integrationgeeksforgeeks+1
Which method is used to handle missing data by replacing them with attribute means or most probable values?
a. Manual deletion
b. Noise filtering
c. Imputation
d. Clustering
Answer: c. Imputationsanfoundry+1
Which step directly follows data acquisition in a typical data science workflow?
a. Data cleaning
b. Data reduction
c. Data splitting
d. Model training
Answer: a. Data cleaningsanfoundry+1
What is the primary goal of data cleaning?
a. Collecting more data
b. Removing duplicates and correcting errors
c. Building machine learning models
d. Reducing data dimensionality
Answer: b. Removing duplicates and correcting errorsplacementpreparation+1
Which of the following is a technique for smoothing noisy data?
a. Binning
b. Sampling
c. Aggregation
d. Clustering
Answer: a. Binningsanfoundry+1
What is data transformation?
a. Altering data structure
b. Converting data into a suitable format
c. Removing outliers
d. Data visualization
Answer: b. Converting data into a suitable formatgtu-mcq+1
Which data preprocessing activity deals with outlier detection and removal?
a. Data sampling
b. Data cleaning
c. Data transformation
d. Data integration
Answer: b. Data cleaninggeeksforgeeks+1
Which of the following is not a data quality dimension?
a. Accuracy
b. Speed
c. Consistency
d. Completeness
Answer: b. Speedsanfoundry
What do you call data that is incorrect or irrelevant in a dataset?
a. Clean data
b. Feature data
c. Noisy data
d. Metadata
Answer: c. Noisy datageeksforgeeks+1
Which of the following methods can be used to handle categorical variables in data preprocessing?
a. Filtering
b. One-hot encoding
c. Aggregation
d. Normalization
Answer: b. One-hot encodingalooba
What is the disadvantage of filling in missing data with a global constant?
a. Reduces size
b. May distort distribution
c. Removes outliers
d. Increases accuracy
Answer: b. May distort distributionsanfoundry
Removing repetitive values in a dataset is known as:
a. Aggregation
b. Normalization
c. Deduplication
d. Sampling
Answer: c. Deduplicationtestbook+1
Which of the following is useful for reducing data volume?
a. Data cleaning
b. Data reduction
c. Data transformation
d. Data integration
Answer: b. Data reductiongtu-mcq+1
Selecting a subset of relevant features for building robust models is called:
a. Feature engineering
b. Feature selection
c. Data integration
d. Data normalization
Answer: b. Feature selectionalooba
Creating new variables based on existing data to improve model accuracy is known as:
a. Feature scaling
b. Feature engineering
c. Data cleaning
d. Data reduction
Answer: b. Feature engineeringalooba
What is the role of data sampling in preprocessing?
a. Increase the data size
b. Reduce computational demand
c. Add noise
d. Merge datasets
Answer: b. Reduce computational demandgtu-mcq
When is data normalization especially crucial?
a. For tree-based models
b. When input features are on different scales
c. When all features are binary
d. When data is already clean
Answer: b. When input features are on different scalesalooba
What is the process of expressing data in summary form for analysis called?
a. Data compression
b. Data aggregation
c. Data fishing
d. Data cleansing
Answer: b. Data aggregationtestbook
Which of the following is not part of the data processing cycle?
a. Input
b. Output
c. Visualization
d. Storage
Answer: c. Visualizationtestbook+1
Which step of preprocessing ensures only one record per entity?
a. Outlier detection
b. Entity resolution
c. Aggregation
d. Transformation
Answer: b. Entity resolutiongeeksforgeeks
The process of handling null values in data is called:
a. Normalization
b. Imputation
c. Binning
d. Deduplication
Answer: b. Imputationsanfoundry
Data cleaning does NOT involve:
a. Removing duplicates
b. Filling missing values
c. Predicting target variables
d. Correcting inconsistencies
Answer: c. Predicting target variablesplacementpreparation+1
Which attribute type requires encoding before machine learning modeling?
a. Numeric
b. Boolean
c. Categorical
d. Integer
Answer: c. Categoricalalooba
What does PCA (Principal Component Analysis) achieve in data processing?
a. Increases feature space
b. Reduces dimensionality
c. Integrates data
d. Cleans noise
Answer: b. Reduces dimensionalitysanfoundry
Which of the following is a disadvantage of manual data cleaning?
a. High speed
b. High accuracy
c. Time consuming
d. Suitable for large datasets
Answer: c. Time consumingsanfoundry
What kind of data pre-processing step is ‘scaling’?
a. Data cleaning
b. Feature transformation
c. Outlier detection
d. Entity extraction
Answer: b. Feature transformationalooba
Which process groups similar data points during preprocessing?
a. Clustering
b. Binning
c. Filtering
d. Integration
Answer: a. Clusteringgeeksforgeeks+1
What is the outcome of properly preprocessed data?
a. Noisy input
b. Poor model accuracy
c. Reliable analysis
d. Increased missing data
Answer: c. Reliable analysissanfoundry+1
‘Disguised missing data’ refers to:
a. Clearly marked null entries
b. Incorrect values entered to avoid disclosing information
c. Unlabeled numerical data
d. Duplicated data
Answer: b. Incorrect values entered to avoid disclosing informationsanfoundry
Data transformation may involve:
a. Encoding categorical features
b. Removing labels
c. Increasing redundancy
d. All of the above
Answer: a. Encoding categorical featuresgeeksforgeeks+1
Filtering extreme values in a dataset is known as:
a. Normalization
b. Outlier removal
c. Sampling
d. Feature selection
Answer: b. Outlier removalsanfoundry+1
Removing unwanted observations from the dataset is called:
a. Feature engineering
b. Pruning
c. Sampling
d. Data cleaning
Answer: d. Data cleaningplacementpreparation+1
Why is standardization important in preprocessing?
a. It increases noise
b. It scales features to zero mean and unit variance
c. It removes all outliers
d. It normalizes categorical data
Answer: b. It scales features to zero mean and unit variancealooba
What term describes combining records that refer to the same entity?
a. Entity resolution
b. Normalization
c. Sampling
d. Discretization
Answer: a. Entity resolutiongeeksforgeeks
What is a drawback of removing all rows with missing values?
a. Increases data quality
b. Increases bias
c. Reduces data volume
d. Both b and c
Answer: d. Both b and csanfoundry
Which type of feature scaling maintains the distribution shape but rescales values?
a. Min-max normalization
b. Mean encoding
c. Sampling
d. Aggregation
Answer: a. Min-max normalizationalooba
Which data transformation method is used for handling skewed features?
a. Log transformation
b. Normalization
c. Sampling
d. Smoothing
Answer: a. Log transformationalooba
Reducing the number of variables under consideration, by obtaining a set of principal variables, refers to:
a. Feature selection
b. Dimensionality reduction
c. Data cleaning
d. Noise removal
Answer: b. Dimensionality reductionsanfoundry
Handling inconsistent formats, such as date and time, is a function of:
a. Data integration
b. Data transformation
c. Data visualization
d. Feature selection
Answer: b. Data transformationgeeksforgeeks
Which encoding technique is suitable when categorical variables have a natural ordering?
a. Label encoding
b. One-hot encoding
c. Binary encoding
d. Normalization
Answer: a. Label encodingalooba
Which of the following is not a cause of noisy data?
a. Machine errors
b. Human errors
c. Outlier detection
d. Data transmission issues
Answer: c. Outlier detectiongeeksforgeeks
What does data reduction specifically avoid in large datasets?
a. Data loss
b. Computational overload
c. Feature selection
d. Data visualization
Answer: b. Computational overloadgtu-mcq+1
What step ensures that data from different sources is mapped to common fields and types?
a. Data cleaning
b. Data transformation
c. Data integration
d. Data reduction
Answer: c. Data integrationgtu-mcq+1
Bucketizing continuous values into discrete bins is known as:
a. Binning
b. Standardization
c. Sampling
d. Encoding
Answer: a. Binningsanfoundry+1
Information about the data itself, such as column names and types, is called:
a. Data
b. Feature
c. Metadata
d. Label
Answer: c. Metadataplacementpreparation+1
Which is not a typical data preprocessing activity?
a. Feature scaling
b. Encoding variables
c. Building neural networks
d. Filling missing values
Answer: c. Building neural networksplacementpreparation+1
Normalizing the range of independent variables is most important for:
a. Decision trees
b. Distance-based algorithms
c. Naive Bayes
d. All algorithms equally
Answer: b. Distance-based algorithmsalooba
Data preprocessing increases:
a. Overfitting
b. Algorithm accuracy and efficiency
c. Algorithm complexity
d. Noise in the dataset
Answer: b. Algorithm accuracy and efficiencysanfoundry+2

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