MCQs

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Overall MCQs for Reference

What is the primary purpose of data cleansing in data science?
a. To remove errors and inconsistencies from the data
b. To encrypt data
c. To increase data size
d. To generate reports
Answer: a. To remove errors and inconsistencies from the datagyansetu
Which of the following is NOT a common method to handle missing values?
a. Deletion
b. Mean imputation
c. Encryption
d. Machine learning techniques
Answer: c. Encryptiongyansetu
What does the ETL process stand for in data preparation?
a. Extract, Transform, Load
b. Edit, Tag, Load
c. Encode, Transfer, Learn
d. Extract, Tag, Load
Answer: a. Extract, Transform, Loadgyansetu
Which function in Excel is commonly used to remove duplicate rows during data cleaning?
a. Remove Duplicates
b. Data Validation
c. Filter
d. Sort
Answer: a. Remove Duplicateswayground
What is the effect of standardization in data preprocessing?
a. Removes missing values
b. Ensures data values are centered around zero
c. Increases model complexity
d. Encrypts data
Answer: b. Ensures data values are centered around zeroplacementpreparation
Which Pandas method is used to remove rows with missing values from a DataFrame?
a. drop_duplicates()
b. dropna()
c. fillna()
d. replace()
Answer: b. dropna()placementpreparation
What is a drawback of filling a missing value with a global constant during data cleansing?
a. Projection of wrong trends in data
b. Reduction in data size
c. Increase in model accuracy
d. Elimination of all missing values
Answer: a. Projection of wrong trends in datasanfoundry
Which task is NOT typically part of data preparation or pre-processing?
a. Data cleansing
b. Data reshaping
c. Data encoding
d. Data encryption
Answer: d. Data encryptionwayground
Data is considered “raw” when:
a. No data has been removed from the dataset
b. All errors are removed
c. Data is normalized
d. Data is visualized
Answer: a. No data has been removed from the datasetwayground
What type of error is addressed by data cleansing?
a. Noise, errors, and incorrect inputs
b. Data visualization issues
c. Encryption errors
d. Model bias
Answer: a. Noise, errors, and incorrect inputsmcqmate
Which measure helps handle outliers effectively in data cleansing?
a. Data transformation
b. Data encryption
c. Image processing
d. Data duplication
Answer: a. Data transformationinterviewkickstart
What does data normalization do?
a. Removes rows
b. Scales data to a standard range
c. Encodes text
d. Makes data discrete
Answer: b. Scales data to a standard rangegyansetu
Which of the following types of data typically requires the most extensive cleaning?
a. Structured data
b. Semi-structured data
c. Unstructured data
d. Normalized data
Answer: c. Unstructured datagyansetu
What is the purpose of parsing text into multiple cells in Excel during cleansing?
a. To improve readability and analysis
b. To encrypt the data
c. To delete duplicates
d. To change data types automatically
Answer: a. To improve readability and analysiswayground
Which method can help fill missing class labels when the label attribute is missing?
a. Ignoring the tuple
b. Generating a copy of the tuple
c. Deleting the label attribute
d. Reversing the tuple
Answer: a. Ignoring the tuplesanfoundry
What is “data smoothing”?
a. Removing noise from data
b. Encrypting data
c. Adding noise to data
d. Rearranging data
Answer: a. Removing noise from datasanfoundry
What issue can the presence of extra spaces in data cause during analysis?
a. Incorrect analysis or categorization
b. Improved accuracy
c. Faster computation
d. Reduced storage
Answer: a. Incorrect analysis or categorizationwayground
Which Excel function removes extra spaces from text data?
a. TRIM
b. CONCATENATE
c. LEFT
d. VALUE
Answer: a. TRIMwayground
Why is it important to convert numbers stored as text to numeric types in data cleansing?
a. To allow numerical analysis
b. To increase text size
c. To delete values
d. To mask data
Answer: a. To allow numerical analysiswayground
Which of the following is NOT a data quality dimension affected by missing data?
a. Completeness
b. Integrity
c. Encryption
d. Accuracy
Answer: c. Encryptionwayground
What does one-hot encoding do in data preprocessing?
a. Converts categorical variables into binary columns
b. Removes duplicates
c. Encodes numbers into strings
d. Normalizes data
Answer: a. Converts categorical variables into binary columnsplacementpreparation
What is a common consequence of storing dates in different formats in a dataset?
a. Inconsistent data
b. Improved analysis
c. Reduced errors
d. Data normalization
Answer: a. Inconsistent datawayground
How do you correctly replace missing values in a Pandas DataFrame column with the mean?
a. df.fillna(df.mean())
b. df.mean().replace()
c. df.replace_mean()
d. df.fill(df.mean())
Answer: a. df.fillna(df.mean())placementpreparation
What tool or process is often used to detect and remove redundant rows from a dataset?
a. Deduplication
b. Encryption
c. Regression
d. Tokenization
Answer: a. Deduplicationgeeksforgeeks
What technique is used to identify and correct or remove corrupt or inaccurate records?
a. Data cleaning
b. Data encryption
c. Data visualization
d. Feature scaling
Answer: a. Data cleaningmcqmate
What is the first step in a Data Science project?
a. Data Collection
b. Problem Definition
c. Data Cleaning
d. Modeling
Answer: b. Problem Definitiongyansetu
What Python feature is commonly used to extract numeric values from text data during cleansing?
a. Regular Expressions (Regex)
b. JSON parsing
c. SQL queries
d. Machine learning
Answer: a. Regular Expressions (Regex)geeksforgeeks
What common problem arises when importing data directly from databases or text files into Excel?
a. All text is cramped into one cell
b. Data gets encrypted
c. Data analysis is automatic
d. Data is lost
Answer: a. All text is cramped into one cellwayground
What does the fillna() function in Pandas do?
a. Fills missing values
b. Deletes duplicate rows
c. Renames columns
d. Normalizes data
Answer: a. Fills missing valuesplacementpreparation
Why is it important to perform data cleaning before applying machine learning models?
a. To improve model accuracy and reliability
b. To reduce data size only
c. To encrypt data
d. To reduce computing power
Answer: a. To improve model accuracy and reliabilitysanfoundry
What is data transformation?
a. Changing data format or structure
b. Encrypting data
c. Removing duplicates
d. Visualizing data
Answer: a. Changing data format or structurewayground
Which of the following can cause discrepancies in data?
a. Too many optional fields in data forms
b. Proper normalization
c. Standard data entry
d. Consistent labels
Answer: a. Too many optional fields in data formssanfoundry
Which of these is an example of a data cleansing activity?
a. Removing redundant records
b. Data modeling
c. Data visualization
d. Feature selection
Answer: a. Removing redundant recordswayground
What is the function of data wrangling?
a. Transforming and mapping raw data into another format
b. Encrypting data
c. Data reduction
d. Data scaling
Answer: a. Transforming and mapping raw data into another formatgyansetu
Which method in Excel can combine text from different cells into one cell during cleaning?
a. CONCATENATE
b. TRIM
c. FIND
d. CLEAN
Answer: a. CONCATENATEwayground
Which of the following is NOT a benefit of data cleansing?
a. Improved data quality
b. Increased data storage requirements
c. Better model performance
d. More accurate analysis
Answer: b. Increased data storage requirementssanfoundry
What is a common cause of missing data in datasets?
a. Data entry errors or system failures
b. Redundant records
c. Encryption
d. Overfitting
Answer: a. Data entry errors or system failuressanfoundry
What type of data inconsistency is caused by storing the same information differently in different places?
a. Redundancy
b. Completeness
c. Accuracy
d. Consistency
Answer: a. Redundancywayground
Which technique is useful to smooth or remove noise from data?
a. Regression smoothing
b. Encryption
c. Data duplication
d. Data categorization
Answer: a. Regression smoothingsanfoundry
What is a major challenge when manually filling missing data?
a. Time consumption and potential errors
b. Data encryption
c. Improved performance
d. Data normalization
Answer: a. Time consumption and potential errorssanfoundry
Which of the following is a valid reason to delete tuples with missing class labels?
a. When missing data is minimal
b. When accuracy is not important
c. When data should be encrypted
d. When datasets are very large
Answer: a. When missing data is minimalsanfoundry
Which of the following is used to combine multiple data sources during preprocessing?
a. Data integration
b. Data deletion
c. Data encryption
d. Data normalization
Answer: a. Data integrationsanfoundry
What tool in Python is commonly used for data cleansing and manipulation?
a. Pandas library
b. TensorFlow
c. Keras
d. Matplotlib
Answer: a. Pandas libraryplacementpreparation
In Excel, which tab contains the “Text to Columns” feature?
a. Data
b. Home
c. Format
d. Review
Answer: a. Datawayground
What is the goal of data duplication removal?
a. To remove redundant data records
b. To create backups
c. To increase dataset size
d. To encrypt data
Answer: a. To remove redundant data recordsgeeksforgeeks
What is an example of a data quality dimension?
a. Consistency
b. Clustering
c. Encryption
d. Visualization
Answer: a. Consistencywayground
What is one challenge when handling dates in datasets?
a. Multiple formats causing inconsistencies
b. Encryption delays
c. Too small data size
d. Data duplication
Answer: a. Multiple formats causing inconsistencieswayground
Which cleaning technique is used to handle noisy data by fitting attributes to a function?
a. Regression
b. Encryption
c. Clustering
d. Scaling
Answer: a. Regressionsanfoundry
What is the effect of failing to clean data properly?
a. Incorrect analysis and poor model performance
b. Reduced data size
c. Increased encryption strength
d. Automated decision-making
Answer: a. Incorrect analysis and poor model performancesanfoundry
What is the typical sequence of data preparation steps?
a. Data cleaning, integration, transformation, reduction
b. Data modeling, mining, encryption, storage
c. Data visualization, reporting, error checking
d. Data encryption, masking, classification
Answer: a. Data cleaning, integration, transformation, reductionwayground

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