MCQs

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Overall MCQs for Reference

What is the purpose of data cleaning in data preparation?
a. To visualize the data
b. To predict outcomes
c. To identify and correct errors or inconsistencies in the dataset
d. To automate machine learning
Answer: c. To identify and correct errors or inconsistencies in the datasetgeeksforgeeks
Which of the following techniques is used for handling missing values?
a. Data normalization
b. Feature selection
c. Filling gaps with mean or most probable value
d. Data encryption
Answer: c. Filling gaps with mean or most probable valuegeeksforgeeks
What is the process of arranging data from multiple sources into a single dataset called?
a. Data reduction
b. Data cleaning
c. Data integration
d. Data binning
Answer: c. Data integrationgeeksforgeeks
Which method groups similar data points together to reduce noise?
a. Regression
b. Clustering
c. Outlier removal
d. Binning
Answer: b. Clusteringgeeksforgeeks
What is the purpose of data transformation?
a. To reduce data storage
b. To convert data into a suitable format for analysis
c. To visualize data
d. To encrypt data
Answer: b. To convert data into a suitable format for analysisgeeksforgeeks
Normalization is best described as:
a. Reducing dataset size
b. Combining data points into summary form
c. Scaling data to a common range
d. Data encryption
Answer: c. Scaling data to a common rangegeeksforgeeks
Which technique transforms continuous data into discrete categories?
a. Normalization
b. Discretization
c. Data compression
d. Sampling
Answer: b. Discretizationgeeksforgeeks
What is the main goal of feature selection?
a. Increase dataset volume
b. Choose most relevant features for analysis
c. Remove useful information
d. Visualize outliers
Answer: b. Choose most relevant features for analysisprojectpro
Filling missing values by using the mean is a method in:
a. Data normalization
b. Data cleaning
c. Data modeling
d. Data compression
Answer: b. Data cleaninggeeksforgeeks
What is the result of data aggregation?
a. Increased complexity
b. Combined summary values
c. Encrypted storage
d. Generation of outliers
Answer: b. Combined summary valuesgeeksforgeeks
What is dimensionality reduction?
a. Encoding text data
b. Reducing the number of variables while retaining information
c. Adding noise to the dataset
d. Grouping data by time
Answer: b. Reducing the number of variables while retaining informationgeeksforgeeks
Data fusion combines information by:
a. Ignoring missing values
b. Integrating inconsistent or incomplete sources
c. Sampling only unique values
d. Deduplicating only
Answer: b. Integrating inconsistent or incomplete sourcesgeeksforgeeks
Removing duplicate entries is called:
a. Aggregation
b. Normalization
c. Deduplication
d. Data fusion
Answer: c. Deduplicationgeeksforgeeks
Imputation is used for:
a. Feature selection
b. Handling missing values
c. Data normalization
d. Data compression
Answer: b. Handling missing valuesgeeksforgeeks
Which of the following methods is NOT used for handling noisy data?
a. Binning
b. Regression
c. Clustering
d. Principal Component Analysis
Answer: d. Principal Component Analysisgeeksforgeeks
What is a feature engineering process?
a. Encoding categorical variables
b. Storing raw data
c. Removing duplicates
d. Creating new data sources
Answer: a. Encoding categorical variablesprojectpro
What is outlier detection important for?
a. Hiding data errors
b. Improving data quality
c. Increasing redundancy
d. Storing more data
Answer: b. Improving data qualitygeeksforgeeks
The process of scaling data to zero mean and unit variance is:
a. Normalization
b. Standardization
c. Discretization
d. Aggregation
Answer: b. Standardizationgeeksforgeeks
Which step organizes data into concept hierarchies?
a. Feature selection
b. Concept hierarchy generation
c. Dimensionality reduction
d. Data binning
Answer: b. Concept hierarchy generationgeeksforgeeks
Which method is commonly used for outlier detection in data?
a. Binning
b. Regression
c. Interquartile Range (IQR)
d. Mean subtraction
Answer: c. Interquartile Range (IQR)itfeature
Data compression is a form of:
a. Data reduction
b. Data integration
c. Feature engineering
d. Data transformation
Answer: a. Data reductiongeeksforgeeks
What is the purpose of data profiling?
a. Encoding sensitive data
b. Assessing data quality and structure
c. Generating new features
d. Adding missing values
Answer: b. Assessing data quality and structurefirsteigen
An example of a categorical feature is:
a. Height in centimeters
b. Temperature in degrees
c. Country name
d. Age
Answer: c. Country nameprojectpro
What does data discovery involve?
a. Encrypting data
b. Identifying key characteristics and patterns in data
c. Reducing dataset size
d. Sorting data alphabetically
Answer: b. Identifying key characteristics and patterns in datafirsteigen
What do you call data that is not arranged in a predefined format?
a. Structured data
b. Unstructured data
c. Numerical data
d. Time-series data
Answer: b. Unstructured datasanfoundry
Which of the following is NOT usually part of data cleaning?
a. Removing duplicates
b. Filling missing values
c. Encoding text
d. Correcting outliers
Answer: c. Encoding textgeeksforgeeks
Sampling in data preparation refers to:
a. Encrypting the dataset
b. Selecting a subset of data for analysis
c. Normalizing numerical values
d. Detecting anomalies
Answer: b. Selecting a subset of data for analysisgeeksforgeeks
Which of the following is a benefit of good data preparation?
a. Increased data errors
b. Improved model performance
c. Higher storage costs
d. Reduced data usability
Answer: b. Improved model performanceprojectpro
Which is a numerical data transformation technique?
a. Label encoding
b. One-hot encoding
c. Min-max scaling
d. Data fusion
Answer: c. Min-max scalinggeeksforgeeks
What is the role of feature extraction in data preparation?
a. Creating entirely new datasets
b. Transforming existing features into lower dimensional ones
c. Removing missing data
d. Visualizing data
Answer: b. Transforming existing features into lower dimensional onesgeeksforgeeks
Data quality is NOT determined by:
a. Accuracy
b. Consistency
c. Completeness
d. Database used
Answer: d. Database usedsanfoundry
Which step merges datasets based on common attributes?
a. Feature selection
b. Data integration
c. Data binning
d. Data normalization
Answer: b. Data integrationgeeksforgeeks
Creating new variables from existing ones is called:
a. Feature engineering
b. Data fusion
c. Standardization
d. Aggregation
Answer: a. Feature engineeringprojectpro
Data that measures temperature is an example of:
a. Nominal data
b. Ordinal data
c. Interval data
d. Categorical data
Answer: c. Interval dataitfeature
Deduplication is vital for:
a. Reducing redundant records
b. Increasing variance
c. Encoding time-series
d. Hiding outliers
Answer: a. Reducing redundant recordsgeeksforgeeks
Which process summarizes multiple values into a single value?
a. Feature selection
b. Aggregation
c. Data fusion
d. Standardization
Answer: b. Aggregationgeeksforgeeks
What is binning used for in data cleaning?
a. Breaking data into equal-sized intervals and smoothing
b. Generating outliers
c. Data encryption
d. Data labeling
Answer: a. Breaking data into equal-sized intervals and smoothinggeeksforgeeks
Which method reduces numeric precision for data reduction?
a. Dimensionality reduction
b. Numerosity reduction
c. Discretization
d. Sampling
Answer: b. Numerosity reductiongeeksforgeeks
What does the data understanding phase focus on?
a. Modeling algorithms
b. Exploring and understanding dataset features
c. Programming
d. Data monetization
Answer: b. Exploring and understanding dataset featuresitfeature
Which of the following is not a data reduction technique?
a. Sampling
b. Aggregation
c. Imputation
d. Feature extraction
Answer: c. Imputationgeeksforgeeks
What is automated feature engineering good for?
a. Reducing computation time in repetitive tasks
b. Generating random noise
c. Data encryption
d. Filling missing values by hand
Answer: a. Reducing computation time in repetitive tasksprojectpro
The main step before deploying a machine learning model is:
a. Data visualization
b. Data preparation
c. Data destruction
d. Data encryption
Answer: b. Data preparationfirsteigen
Structured data is:
a. Data not in rows or columns
b. Data arranged in tables (rows and columns)
c. Random text
d. Images
Answer: b. Data arranged in tables (rows and columns)sanfoundry
Which metric is NOT typically used to detect outliers?
a. Z-score
b. Interquartile Range
c. Median
d. Average
Answer: d. Averageitfeature
What is the main benefit of removing outliers?
a. Increase data volume
b. Improve analysis accuracy
c. Reduce redundancy
d. Increase noise
Answer: b. Improve analysis accuracygeeksforgeeks
What is the first step in data preparation?
a. Model selection
b. Data collection
c. Feature extraction
d. Data encoding
Answer: b. Data collectionfirsteigen
Transforming categorical variables into numerical values can be done using:
a. One-hot encoding
b. Data fusion
c. Binning
d. Sampling
Answer: a. One-hot encodingprojectpro
Ensuring uniform measurement units is called:
a. Integration
b. Standardization
c. Data profiling
d. Aggregation
Answer: b. Standardizationgeeksforgeeks
What process helps unify datasets with different schema?
a. Data mapping
b. Normalization
c. Feature extraction
d. Data encryption
Answer: a. Data mappingfirsteigen
After data preparation, the next logical phase is:
a. Data integration
b. Data modeling
c. Data cleaning
d. Data collection
Answer: b. Data modelingfirsteigen

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