MCQs

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Overall MCQs for Reference

What is the first step in preparing data for analysis?
a. Data transformation
b. Data collection
c. Data cleaning
d. Data integration
Answer: b. Data collectionpecan+1
Which process corrects or removes inconsistent values from a dataset?
a. Data sampling
b. Data cleaning
c. Data splitting
d. Data loading
Answer: b. Data cleaningfirsteigen+1
Which term describes converting categorical variables to numerical ones?
a. Normalization
b. Label encoding
c. Feature scaling
d. Data reduction
Answer: b. Label encodingdatacamp+1
Feature engineering’s main goal is to:
a. Gather data
b. Transform raw data into features
c. Visualize data
d. Analyze outcomes
Answer: b. Transform raw data into featuressanfoundry+1
What technique selects the most relevant features for modeling?
a. Feature selection
b. Data transformation
c. Data integration
d. Dimensionality reduction
Answer: a. Feature selectionplacementpreparation+1
Removing duplicate records from a database is known as:
a. Feature engineering
b. Data cleaning
c. Data validation
d. Data enrichment
Answer: b. Data cleaninginterviewbit+1
What ensures numeric variables have the same range or scale?
a. Data reduction
b. Feature encoding
c. Normalization
d. Feature selection
Answer: c. Normalizationpecan+1
One-hot encoding is used for:
a. Numerical scaling
b. Transforming categorical variables
c. Removing outliers
d. Reducing dataset size
Answer: b. Transforming categorical variablesdatacamp+1
What is the purpose of sampling data?
a. To increase dataset size
b. To decrease dataset size
c. To select a representative subset
d. To remove noise
Answer: c. To select a representative subsetinterviewkickstart+1
Which technique reduces dataset dimensionality?
a. Data integration
b. Data cleaning
c. Feature scaling
d. Principal Component Analysis
Answer: d. Principal Component Analysisplacementpreparation+1
Which step deals with missing values in the data?
a. Data sampling
b. Feature selection
c. Data imputation
d. Data transformation
Answer: c. Data imputationfirsteigen+1
What describes combining data from multiple sources?
a. Data enrichment
b. Data integration
c. Feature extraction
d. Data validation
Answer: b. Data integrationbyjus+1
The act of splitting data into training and testing sets is known as:
a. Data wrangling
b. Data splitting
c. Data reducing
d. Feature selection
Answer: b. Data splittingplacementpreparation+1
Which term refers to preparing data for analysis by cleaning and transforming it?
a. Data wrangling
b. Data visualization
c. Data modeling
d. Data mining
Answer: a. Data wranglinggyansetu+1
Data selection usually involves:
a. Taking irrelevant data
b. Filtering relevant data
c. Randomizing data
d. Visualizing data
Answer: b. Filtering relevant datainterviewbit+1
Which method is commonly used to handle outliers?
a. Removing them
b. Normalizing data
c. Imputation
d. All of the above
Answer: d. All of the abovefirsteigen+1
Label encoding transforms:
a. Numerical data to labels
b. Categorical data to numbers
c. Text to image
d. Image to text
Answer: b. Categorical data to numberspecan+1
What is meant by ‘Big Data’?
a. Large emails
b. Data that fits in Excel
c. Extremely large datasets
d. Zoomed images
Answer: c. Extremely large datasetsgyansetu
Data profiling involves:
a. Checking data statistics
b. Encoding variables
c. Feature selection
d. Data modeling
Answer: a. Checking data statisticsdatacamp+1
Which preprocessing step makes categorical variables suitable for numerical models?
a. Scaling
b. One-hot encoding
c. Outlier detection
d. Sampling
Answer: b. One-hot encodingplacementpreparation+1
What is batch size in deep learning?
a. Number of datasets
b. Number of epochs
c. Samples processed before model update
d. Size of memory
Answer: c. Samples processed before model updategyansetu
What is the effect of standardization in preprocessing?
a. Removes duplicates
b. Centers data around zero
c. Improves cleaning
d. Removes missing values
Answer: b. Centers data around zeroplacementpreparation
Which technique is used to handle imbalanced datasets?
a. Upsampling
b. Feature selection
c. Data cleaning
d. Principal Component Analysis
Answer: a. Upsamplingpecan+1
Which process deals with noisy and inconsistent data?
a. Data cleaning
b. Data transformation
c. Data reduction
d. Data integration
Answer: a. Data cleaninggtu-mcq+1
What is feature scaling?
a. Adding features
b. Converting categories to numbers
c. Bringing all values to the same scale
d. Removing irrelevant features
Answer: c. Bringing all values to the same scaledatacamp+1
Data enrichment involves:
a. Adding relevant information
b. Shrinking the dataset
c. Removing features
d. Encoding variables
Answer: a. Adding relevant informationfirsteigen
Which action identifies data properties?
a. Data wrangling
b. Data mining
c. Data cleaning
d. Both a and b
Answer: b. Data mininginterviewkickstart
What is a critical factor in choosing a node during tree construction?
a. Highest information gain
b. Most records
c. Lowest information gain
d. High entropy
Answer: a. Highest information gaininterviewkickstart
What is the full form of DMQL in data mining?
a. Data Marts Query Language
b. DBMiner Query Language
c. Dataset Mining Query Language
d. Data Mining Query Language
Answer: d. Data Mining Query Languagebyjus
Data reduction methods help with:
a. Data visualization
b. Reducing data complexity
c. Adding irrelevant data
d. Data transformation
Answer: b. Reducing data complexitygtu-mcq+1
After standardizing, model performance drops. What’s a possible issue?
a. Data leakage
b. Overfitting
c. Outliers
d. Incorrect scaling
Answer: d. Incorrect scalingplacementpreparation
Which data operation is not usually part of shaping data?
a. Data cleaning
b. Feature selection
c. Data corruption
d. Data transformation
Answer: c. Data corruptionpecan+1
What does data validation ensure?
a. Data is ready for modeling
b. Data is wrongly encoded
c. Data contains duplicates
d. Data visualization is better
Answer: a. Data is ready for modelingfirsteigen
Which step in shaping data includes filling in missing values using techniques?
a. Data visualization
b. Data imputation
c. Data wrangling
d. Data sampling
Answer: b. Data imputationfirsteigen+1
Which method transforms non-numeric variables for machine learning?
a. Feature selection
b. Feature encoding
c. Data integration
d. Data cleaning
Answer: b. Feature encodingdatacamp+1
Exploratory Data Analysis focuses on:
a. Estimating accuracy
b. Visualizing and summarizing data
c. Removing records
d. Feature selection
Answer: b. Visualizing and summarizing datagyansetu
Which step involves reformatting and combining datasets?
a. Data cleaning
b. Data preparation
c. Data modeling
d. Data transformation
Answer: b. Data preparationtalend+1
Data wrangling can also be called:
a. Data exploring
b. Data shaping
c. Data mining
d. Data integrating
Answer: b. Data shapinggyansetu+1
In shaping data, processing outliers is a part of:
a. Data cleaning
b. Data transformation
c. Feature extraction
d. Data profiling
Answer: a. Data cleaningplacementpreparation+1
Dimensionality reduction technique often used is:
a. Feature scaling
b. Principal Component Analysis
c. Data mining
d. Data sampling
Answer: b. Principal Component Analysispecan+1
Data splitting is essential because:
a. It mixes the dataset
b. It helps evaluate model generalization
c. It removes null values
d. It visualizes outcomes
Answer: b. It helps evaluate model generalizationdatacamp+1
What describes transforming raw data into meaningful features?
a. Data integration
b. Feature engineering
c. Data selection
d. Data visualization
Answer: b. Feature engineeringsanfoundry+1
Which is not recommended in data shaping?
a. Removing duplicates
b. Feature extraction
c. Introducing noise
d. Feature engineering
Answer: c. Introducing noisegtu-mcq+1
Which transformation makes variables comparable by their means and variances?
a. One-hot encoding
b. Normalization
c. Standardization
d. Data splitting
Answer: c. Standardizationdatacamp+1
Where do you go to modify and transform data in Power BI Desktop?
a. Data Transformation
b. Data Editing
c. Question Editor
d. Data Modeling
Answer: b. Data Editinggyansetu
Which process uses techniques like one-hot or label encoding?
a. Feature extraction
b. Dimensionality reduction
c. Feature encoding
d. Data integration
Answer: c. Feature encodingpecan+1
The presence of missing values in a dataset requires:
a. Data validation
b. Data imputation
c. Data transformation
d. Feature engineering
Answer: b. Data imputationfirsteigen+1
Shaping data is required to:
a. Make data suitable for analysis
b. Make data unreadable
c. Visualize data
d. Store data efficiently
Answer: a. Make data suitable for analysispecan+1
When shaping or transforming data in Power BI Desktop, what happens to source data?
a. It is deleted
b. It is appended
c. Remains unchanged
d. Is transformed
Answer: c. Remains unchangedgyansetu
What is the main goal of data shaping?
a. To clean raw data
b. To prepare data for modeling
c. To visualize trends
d. To split the dataset
Answer: b. To prepare data for modelingfirsteigen+1

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