MCQs

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Overall MCQs for Reference

Which of the following is not important in determining data quality?
a. Accuracy
b. Consistency
c. Completeness
d. Database
Answer: d. Databasesanfoundry
Sometimes users submit incorrect data in required fields to avoid divulging information. This is known as:
a. Disguised missing data
b. Organized missing data
c. Characteristic missing data
d. Coordinated missing data
Answer: a. Disguised missing datasanfoundry
Data cleaning involves filling in missing values.
a. True
b. False
Answer: a. Truesanfoundry
Data integration is not a step in data preprocessing.
a. True
b. False
Answer: b. Falsesanfoundry
Which of the following is not true about data reduction?
a. Reduced data strives to give same analytical results as the original data
b. Reduced data gives less accurate analytical results
c. It involves dimensionality reduction
d. It involves numerosity reduction
Answer: b. Reduced data gives less accurate analytical resultssanfoundry
Which of the following is not a form of data transformation?
a. Normalization
b. Discretization
c. Concept hierarchy
d. Compression
Answer: d. Compressionsanfoundry
What is the process of converting data into a structured format suitable for analysis and storage called?
a. Data transformation
b. Data validation
c. Data normalization
d. Data mining
Answer: a. Data transformationexamveda
Which step in data preprocessing removes duplicate records?
a. Data reduction
b. Data cleaning
c. Data transformation
d. Discretization
Answer: b. Data cleaningexamveda
What is the primary goal of data preprocessing in the context of machine learning?
a. Collecting more data
b. Storing data
c. Improving the data quality for better analysis
d. Developing algorithms
Answer: c. Improving the data quality for better analysisexamveda
Which of these is used to convert categorical data into binary format during preprocessing?
a. Normalization
b. One-hot encoding
c. Label encoding
d. Discretization
Answer: b. One-hot encodingexamveda
Which of the following is NOT a step in data pre-processing?
a. Data cleaning
b. Data reduction
c. Data transformation
d. Data visualization
Answer: d. Data visualizationyoutube
What is one common method to handle missing data in a dataset?
a. Remove the entire dataset
b. Fill with zeros
c. Estimate values based on available data
d. Leave missing
Answer: c. Estimate values based on available dataexamveda
The process of selecting a representative subset of a larger dataset for analysis is called:
a. Smoothing
b. Sampling
c. Binning
d. Transformation
Answer: b. Samplingexamveda
Which data collection method involves direct interaction with subjects?
a. Indirect measurement
b. Sensor data
c. Surveys/interviews
d. Data mining
Answer: c. Surveys/interviewsexamveda
What is the term for data that is in its original, unprocessed form?
a. Raw data
b. Normalized data
c. Standardized data
d. Cleaned data
Answer: a. Raw dataexamveda
Which is NOT a common data preprocessing technique?
a. Normalization
b. Data cleaning
c. Data visualization
d. Encoding
Answer: c. Data visualizationexamveda
Data normalization in preprocessing is used to:
a. Remove outliers
b. Scale feature values to a standard range
c. Convert text to numbers
d. Increase data size
Answer: b. Scale feature values to a standard rangesanfoundry
Which step is used to combine data from multiple sources?
a. Data cleaning
b. Data integration
c. Data transformation
d. Data reduction
Answer: b. Data integrationsanfoundry
Which of the following helps reduce data dimensionality?
a. Sampling
b. Feature selection
c. Data merging
d. Data cleansing
Answer: b. Feature selectionsanfoundry
KNN imputation is used for:
a. Noise removal
b. Outlier detection
c. Filling missing values
d. Data aggregation
Answer: c. Filling missing valuessaidatascience
Which method transforms skewed data distributions into more normal distributions?
a. One-hot encoding
b. Label encoding
c. Logarithmic transformation
d. Smoothing
Answer: c. Logarithmic transformationsaidatascience
Outliers are typically identified using:
a. Mean
b. Median
c. Z-score or IQR method
d. Random sampling
Answer: c. Z-score or IQR methodsaidatascience
What is the main objective of data cleaning?
a. Enhance dataset size
b. Remove or fix errors and inconsistencies
c. Encode data
d. Visualize data
Answer: b. Remove or fix errors and inconsistenciessanfoundry
Label encoding is mostly used with:
a. Ordinal categorical features
b. Continuous numerical features
c. Images
d. Strings
Answer: a. Ordinal categorical featuressaidatascience
PCA (Principal Component Analysis) is used for:
a. Feature extraction and dimensionality reduction
b. Data cleaning
c. Outlier removal
d. Label encoding
Answer: a. Feature extraction and dimensionality reductionsaidatascience
What technique can smooth noisy data?
a. Binning
b. Filling missing values
c. Label encoding
d. Sampling
Answer: a. Binningsanfoundry
Which is a method for treating duplicate records?
a. Sampling
b. Integration
c. Deduplication
d. Normalization
Answer: c. Deduplicationexamveda
Which step involves changing categorical variables into binary ones?
a. Normalization
b. One-hot encoding
c. Sampling
d. Reducing
Answer: b. One-hot encodingsaidatascience
Smoothing the data helps:
a. Increase noise
b. Reduce noise and outliers
c. Add complexity
d. Split data
Answer: b. Reduce noise and outlierssanfoundry
Standardization differs from normalization by:
a. Making the mean = 0 and SD = 1
b. Scaling between 0 and 1
c. Removing outliers
d. Binning data
Answer: a. Making the mean = 0 and SD = 1saidatascience
Which step in data preprocessing deals with inconsistent data entry (e.g., “yes”, “YES”, “Yes”)?
a. Data cleaning
b. Integration
c. Normalization
d. Feature engineering
Answer: a. Data cleaningsanfoundry
Min-Max normalization scales data to which range?
a. -1 to 1
b. 0 to 1
c. 1 to 10
d. 0.1 to 1.0
Answer: b. 0 to 1saidatascience
Reducing the number of features in a dataset is called:
a. Feature selection
b. Sampling
c. Aggregation
d. Cleaning
Answer: a. Feature selectionsanfoundry
The term for an extreme data value deviating from other observations:
a. Outlier
b. Sample
c. Mean
d. Standard Deviation
Answer: a. Outliersaidatascience
Which technique combines numerical and categorical cleansing?
a. Data wrangling
b. Data pruning
c. One-hot encoding
d. Data mining
Answer: a. Data wranglingsaidatascience
Data transformation techniques include:
a. Normalization
b. Scaling
c. Discretization
d. All of the above
Answer: d. All of the abovesanfoundry
Which of these is a reason for handling missing data?
a. Improve analysis accuracy
b. Increase data size
c. Generate more labels
d. Reduce processing speed
Answer: a. Improve analysis accuracysanfoundry
What does “noise” refer to in data preprocessing?
a. Unwanted variations or errors in data
b. Outliers only
c. Only missing data
d. Visualization artifacts
Answer: a. Unwanted variations or errors in datasanfoundry
Choosing a subset of variables relevant for data analysis is called:
a. Feature selection
b. Normalization
c. Sampling
d. Outlier detection
Answer: a. Feature selectionsanfoundry
What does discretization refer to in data pre-processing?
a. Dividing continuous data into intervals
b. Increasing dataset size
c. Integrating datasets
d. Adding noise
Answer: a. Dividing continuous data into intervalssanfoundry
Normalization helps in:
a. Bringing values to a common scale
b. Encoding categorical data
c. Increasing dataset size
d. Treating outliers
Answer: a. Bringing values to a common scalesanfoundry
Which step combines datasets from multiple databases?
a. Integration
b. Cleaning
c. Selection
d. Binning
Answer: a. Integrationsanfoundry
What is a common consequence of not handling missing data properly?
a. Reduced model accuracy
b. Increased visually appealing charts
c. Better performance
d. Improved data storage
Answer: a. Reduced model accuracysanfoundry
“Data wrangling” is also known as:
a. Data munging
b. Data labeling
c. Data mining
d. Data storing
Answer: a. Data mungingsaidatascience
The process of combining multiple values into a single value in a dataset is called:
a. Aggregation
b. Normalization
c. Outlier detection
d. Encoding
Answer: a. Aggregationsanfoundry
What is the main output of the data preprocessing stage?
a. Cleaned and transformed data
b. Raw data
c. Model parameters
d. Charts
Answer: a. Cleaned and transformed datasanfoundry
Which method is used to scale features based on their median and IQR?
a. Robust scaling
b. Min-max scaling
c. Z-score scaling
d. Logarithmic scaling
Answer: a. Robust scalingsaidatascience
If data is highly skewed, which transformation is preferred?
a. Log transformation
b. Min-max scaling
c. One-hot encoding
d. Integration
Answer: a. Log transformationsaidatascience
The process to reduce the number of records in the dataset is called:
a. Sampling
b. Feature selection
c. Normalization
d. Cleaning
Answer: a. Samplingexamveda
What is the first step in any data preprocessing pipeline?
a. Data cleaning
b. Data visualization
c. Data modeling
d. Data reduction
Answer: a. Data cleaningsanfoundry

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