MCQs

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Overall MCQs for Reference

What is the main objective of data cleaning in a dataset?
a. To increase data size
b. To reduce computational speed
c. To remove noise and errors
d. To add missing values
Answer: c. To remove noise and errorsmcqmate
Which method can handle missing values in a dataset?
a. Deleting rows
b. Imputation
c. Using default values
d. All of the above
Answer: d. All of the abovegeeksforgeeks
Data cleaning is primarily performed in which stage of the data science process?
a. Data collection
b. Data cleaning and exploration
c. Model training
d. Evaluation
Answer: b. Data cleaning and explorationinterviewbit
What is the effect of having duplicate records in a dataset?
a. Improves data quality
b. Leads to redundant processing
c. Reduces overfitting
d. None of the above
Answer: b. Leads to redundant processinggeeksforgeeks
Which Python library is commonly used for data cleaning?
a. TensorFlow
b. Pandas
c. Keras
d. PyTorch
Answer: b. Pandasgeeksforgeeks
Which of the following is NOT a benefit of cleaning data?
a. Improved model accuracy
b. Reduced interpretability
c. Reliable analysis
d. Consistent results
Answer: b. Reduced interpretabilitygeeksforgeeks
Removing outliers can help to:
a. Distort the data
b. Increase noise
c. Improve model performance
d. Randomly sample data
Answer: c. Improve model performancegeeksforgeeks
Which function in Pandas removes duplicate rows?
a. drop()
b. dropna()
c. drop_duplicates()
d. fillna()
Answer: c. drop_duplicates()geeksforgeeks
What is the best approach if a categorical variable has missing values?
a. Discard the variable
b. Impute with the mode
c. Fill with zero
d. Normalize the variable
Answer: b. Impute with the modegeeksforgeeks
Why is it important to address missing values before model training?
a. To introduce more noise
b. To prevent biases and errors
c. To delay the process
d. To select random examples
Answer: b. To prevent biases and errorsgeeksforgeeks
Which of the following is a common data cleaning technique?
a. Encoding labels
b. Feature engineering
c. Handling missing values
d. Model deployment
Answer: c. Handling missing valuesplacementpreparation
The replacement of all missing numerical values with the column mean is called:
a. Mode imputation
b. Mean imputation
c. Median imputation
d. Zero imputation
Answer: b. Mean imputationplacementpreparation
What is the result of not cleaning a dataset before analysis?
a. Increased accuracy
b. Noise and inconsistencies
c. Improved interpretability
d. Enhanced reliability
Answer: b. Noise and inconsistenciesgeeksforgeeks
Which type of data cleaning removes variables with no predictive value?
a. Feature selection
b. Feature extraction
c. Overfitting
d. Data integration
Answer: a. Feature selectionplacementpreparation
What does standardizing data do?
a. Converts text to numbers
b. Ensures values are centered around zero
c. Removes categorical values
d. Splits dataset
Answer: b. Ensures values are centered around zeroplacementpreparation
To ensure all categorical variables are suitable for machine learning models, one should use:
a. Scaling
b. One-hot encoding
c. Normalization
d. Feature importance
Answer: b. One-hot encodingplacementpreparation
What Pandas function is used to remove rows with missing values?
a. drop_duplicates()
b. dropna()
c. fillna()
d. replace()
Answer: b. dropna()placementpreparation
Which method helps to handle irrelevant features in a large dataset?
a. Feature selection
b. Cross-validation
c. Encoding
d. Train-test split
Answer: a. Feature selectionplacementpreparation
What is data imputation?
a. Inserting synthetic data
b. Filling missing data with valid values
c. Removing columns
d. Scaling features
Answer: b. Filling missing data with valid valuesgeeksforgeeks
What technique is used to detect and handle extreme values in data?
a. Thresholding
b. Outlier detection
c. Label encoding
d. Data sorting
Answer: b. Outlier detectiongeeksforgeeks
How can you ensure every value in a column uses the same unit for distance?
a. Data grouping
b. Standardization
c. Unit normalization
d. Data transformation
Answer: c. Unit normalizationmlexam
If the ‘Unnamed: 0’ column in a dataset is unnecessary, what should you do?
a. Scale it
b. Encode it
c. Remove it
d. Replace values
Answer: c. Remove itgeeksforgeeks
Why is preprocessing essential for web-scraped datasets?
a. Scrapes more data
b. Ensures data is clean and ready for analysis
c. Converts data to images
d. Makes dataset smaller
Answer: b. Ensures data is clean and ready for analysisgeeksforgeeks
Which is the first step of data cleaning?
a. Model training
b. Removing duplicates
c. Analysis
d. Feature extraction
Answer: b. Removing duplicatesgeeksforgeeks
Which of the following is considered structured data?
a. Text documents
b. Images
c. Data in spreadsheets
d. Videos
Answer: c. Data in spreadsheetssanfoundry
In data cleaning, what does ‘type conversion’ generally refer to?
a. Changing numeric values to text
b. Changing data formats for consistency
c. Adding more columns
d. Dropping duplicates
Answer: b. Changing data formats for consistencymlexam
Which Python function counts unique values in a column?
a. unique()
b. nunique()
c. value_counts()
d. count()
Answer: b. nunique()geeksforgeeks
What is the impact of noisy data on machine learning models?
a. Leads to robust models
b. Has no impact
c. Decreases model accuracy
d. Removes overfitting
Answer: c. Decreases model accuracygeeksforgeeks
An irrelevant data attribute in a dataset should be:
a. Kept for future use
b. Dropped
c. Duplicated
d. Randomized
Answer: b. Droppedmlexam
Which of these is a common cause for inconsistent data entries?
a. Proper validation
b. Standard naming conventions
c. Manual data entry
d. Automated pipelines
Answer: c. Manual data entrymlexam
How do you handle incorrectly formatted date fields in a dataset?
a. Remove dates
b. Convert to consistent string format
c. Convert to datetime type
d. Leave as is
Answer: c. Convert to datetime typemlexam
What is meant by ‘fuzzy matching’ in dataset cleaning?
a. Exact row comparison
b. Approximate matching of similar values
c. Binary search
d. Sorting rows
Answer: b. Approximate matching of similar valuesmlexam
Which of these is a valid approach to impute missing categorical data?
a. Mean imputation
b. Losing the value
c. Mode imputation
d. Zero fill
Answer: c. Mode imputationgeeksforgeeks
Which method standardizes columns with different scales?
a. Normalization
b. Optimization
c. Grouping
d. Data selection
Answer: a. Normalizationplacementpreparation
What is ‘feature engineering’?
a. Creating new variables from existing data
b. Removing outliers
c. Model evaluation
d. Sample splitting
Answer: a. Creating new variables from existing dataplacementpreparation
What is the main goal of Exploratory Data Analysis (EDA)?
a. Improve code performance
b. Understand and summarize data
c. Remove all missing values
d. Train models
Answer: b. Understand and summarize datageeksforgeeks
What happens if you ignore outliers during data cleaning?
a. Skewed analysis
b. Balanced accuracy
c. Improved consistency
d. Enhanced model training
Answer: a. Skewed analysisgeeksforgeeks
What type of data does cleaning text typically deal with?
a. Numeric
b. Categorical
c. Unstructured
d. Time series
Answer: c. Unstructuredsanfoundry
Which step involves ensuring consistent capitalization in text data?
a. Feature selection
b. Text normalization
c. Standard deviation
d. Hot encoding
Answer: b. Text normalizationgeeksforgeeks
Removing rows where all values are missing is called:
a. Removing duplicates
b. Row deletion
c. Complete case analysis
d. Data transformation
Answer: c. Complete case analysisgeeksforgeeks
What does ‘noise’ mean in the context of datasets?
a. Additional features
b. Irrelevant or random errors
c. Standard values
d. Required rows
Answer: b. Irrelevant or random errorsmcqmate
What does handling inconsistencies in data refer to?
a. Deleting the dataset
b. Ensuring uniform data formats and units
c. Adding random values
d. Model fitting
Answer: b. Ensuring uniform data formats and unitsgeeksforgeeks
Which Pandas method fills NA/NaN values with specified values?
a. dropna()
b. drop_duplicates()
c. fillna()
d. append()
Answer: c. fillna()placementpreparation
What method can be used to reduce dimensionality before cleaning?
a. PCA
b. Linear scaling
c. Data collection
d. Duplication
Answer: a. PCAplacementpreparation
Purpose of validating input data during entry is:
a. Minimize data redundancy
b. Prevent incorrect formats
c. Cause more missing values
d. Eliminate outliers
Answer: b. Prevent incorrect formatsmlexam
What is the effect of leaving irrelevant features in a training set?
a. Improves learning
b. Slows computation and misleads models
c. Ensures completeness
d. Decreases redundancy
Answer: b. Slows computation and misleads modelsplacementpreparation
Which method can identify duplicate entries efficiently in large Pandas DataFrames?
a. equals()
b. duplicated()
c. concat()
d. append()
Answer: b. duplicated()geeksforgeeks
The best action for columns with too many missing values is typically:
a. Keep as is
b. Fill with the average
c. Remove the columns
d. Encode with the median
Answer: c. Remove the columnsgeeksforgeeks
A ‘global constant’ fill for missing data can:
a. Provide unique imputation
b. Skew trends and introduce bias
c. Increase accuracy
d. Be used for all types of data
Answer: b. Skew trends and introduce biassanfoundry
Which data cleaning method is best for continuous variables with missing values?
a. Forward fill
b. Mean/median imputation
c. Label encoding
d. Random sampling
Answer: b. Mean/median imputationgeeksforgeeks

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