Data Cleaning and Preparation for Big Data Analytics: Master Data Preprocessing Techniques

Question 1

Which task is NOT a core part of data preprocessing?

Accepted Answer

Creating data visualizations

Answer

Identifying and removing errors

Answer

Transforming data into a consistent format

Answer

Handling missing data

Question 2

In data cleaning for big data analytics, which technique is commonly used to handle missing values?

Accepted Answer

Imputation

Answer

Sampling

Answer

Aggregation

Answer

Visualization

Question 3

In big data analytics, which technique is commonly used to rectify errors?

Accepted Answer

Data scrubbing

Answer

Data imputation

Answer

Data normalization

Answer

Data validation

Question 4

In the context of data preprocessing and cleaning, which technique is not commonly used?

Accepted Answer

Data clustering

Answer

Data imputation

Answer

Data normalization

Answer

Data validation

Question 5

Which of the following is a potential drawback of data preprocessing?

Accepted Answer

Increased computational requirements

Answer

Enhanced data quality

Answer

Improved data accuracy

Answer

Reduced data volume

Question 6

Which data transformation technique is specifically designed to convert categorical data into numerical data, making it suitable for analysis?

Accepted Answer

One-hot encoding

Answer

Normalization

Answer

Imputation

Answer

Standardization

Question 7

In big data analytics, which technique is employed to detect and eliminate errors?

Accepted Answer

Data scrubbing

Answer

Data transformation

Answer

Data integration

Answer

Data reduction

Question 8

Which data cleaning technique standardizes data to enhance comparability?

Accepted Answer

Data normalization

Answer

Data aggregation

Answer

Data mining

Answer

Data visualization

Question 9

Which of the following is **not** a data cleaning technique used to prepare data for analysis?

Accepted Answer

Enforcing data quality rules

Answer

Removing duplicate data

Answer

Imputing missing values

Answer

Transforming data to a consistent format

Question 10

In the context of big data, which technique is commonly employed to remove data points that deviate significantly from the rest of the data?

Accepted Answer

Trimming

Answer

Normalization

Answer

Binning

Answer

Smoothing

Question 11

Which data preprocessing technique is specifically designed to eliminate duplicate data records from a dataset?

Accepted Answer

De-duplication

Answer

Normalization

Answer

Discretization

Answer

Imputation

Question 12

Which data preprocessing technique converts raw big data into a consistent format suitable for analysis?

Accepted Answer

Data transformation

Answer

Data cleaning

Answer

Data integration

Answer

Data reduction

Question 13

Which technique is used to eliminate duplicate records during big data preprocessing?

Accepted Answer

Data Deduplication

Answer

Data Imputation

Answer

Data Standardization

Answer

Data Validation

Question 14

Which of the following is NOT a method used for data imputation, where missing values are replaced?

Accepted Answer

Random sampling

Answer

Mean imputation

Answer

Median imputation

Answer

Mode imputation

Question 15

When cleaning data, which technique is used to handle missing values?

Accepted Answer

Data imputation

Answer

Data transformation

Answer

Data reduction

Answer

Data normalization

Question 16

In the data preprocessing and cleaning workflow, which stage is the most crucial, laying the groundwork for all subsequent steps?

Accepted Answer

Data preprocessing

Answer

Data collection

Answer

Data cleaning

Answer

Data analysis

Question 17

Which technique is commonly used to effectively detect outliers in a dataset?

Accepted Answer

Isolation Forest

Answer

Clustering

Answer

Regression Analysis

Answer

Principal Component Analysis

Question 18

Which data normalization technique transforms numerical data into a range between 0 and 1, mapping the minimum value to 0 and the maximum value to 1?

Accepted Answer

Min-max normalization

Answer

Z-score normalization

Answer

Max-abs normalization

Answer

Decimal scaling

Question 19

Which data preprocessing technique is specifically designed to detect and address inconsistencies within big data?

Accepted Answer

Data Validation

Answer

Data Normalization

Answer

Data Standardization

Question 20

Which data imputation technique utilizes statistical modeling to estimate missing values by analyzing the relationships between variables in the dataset?

Accepted Answer

Multiple Imputation by Chained Equations (MICE)

Answer

Mean Imputation

Answer

K-Nearest Neighbors Imputation

Answer

Random Imputation

Question 21

Which technique is primarily used to transform data rather than clean it?

Accepted Answer

Data normalization

Answer

Data scrubbing

Answer

Data imputation

Answer

Data validation

Question 22

In data preprocessing, which technique is used to convert non-numeric data into a numerical format for analysis?

Accepted Answer

Encoding

Answer

Normalization

Answer

Imputation

Answer

Discretization

Question 23

In the context of big data analytics, which technique is primarily used to detect and remove duplicate entries from a massive dataset?

Accepted Answer

Data deduplication

Answer

Data aggregation

Question 24

In big data cleaning, which technique is used to remove duplicate rows from a dataset?

Accepted Answer

Deduplication

Answer

Subsetting

Answer

Aggregation

Answer

Imputation

Question 25

In data preprocessing, which tool is specifically designed for data wrangling, a process that involves transforming and cleaning data before analysis?

Accepted Answer

OpenRefine

Answer

Apache Hadoop

Answer

Apache Spark

Answer

KNIME

Question 26

In the data preprocessing and cleaning phase of a big data analytics project, which technique is commonly used to transform data?

Accepted Answer

Data normalization

Answer

Data imputation

Answer

Data integration

Question 27

Which technique is used to handle missing data?

Accepted Answer

Data imputation

Answer

Data transformation

Answer

Data integration

Answer

Data reduction

Question 28

In data analytics, which technique converts categorical variables into numerical values that represent different categories?

Accepted Answer

Encoding

Answer

Normalization

Answer

Imputation

Answer

Standardization

Question 29

In data scrubbing, which technique is specifically designed to rectify data errors and inconsistencies?

Accepted Answer

Data Error Correction

Answer

Data Deduplication

Answer

Data Transformation

Answer

Data Validation

Question 30

In data cleaning, which technique is employed to fill in missing values within a dataset?

Accepted Answer

Data imputation

Answer

Data smoothing

Question 31

In big data analytics, which technique combines data from multiple sources into a single, comprehensive dataset for analysis?

Accepted Answer

Data Merging

Answer

Data Extraction

Answer

Data Scrubbing

Answer

Data Validation

Question 32

In data preprocessing, which technique is commonly used to transform data?

Accepted Answer

Data normalization

Answer

Data imputation

Answer

Data validation

Question 33

In big data analytics, which technique is primarily used to select the most relevant features for prediction or analysis by eliminating less significant ones?

Accepted Answer

Feature Selection

Answer

Sampling

Answer

Binning

Answer

Dimensionality Reduction

Question 34

In data imputation, which technique estimates missing values based on similar data points?

Accepted Answer

K-Nearest Neighbors (KNN) Imputation

Answer

Mean Imputation

Answer

Random Imputation

Answer

Regression Imputation

Question 35

In big data analytics, what fundamental issue stems from the challenges of handling large and complex datasets, leading to data inconsistencies and inaccuracies?

Accepted Answer

Data Inconsistency

Answer

Data Redundancy

Answer

Data Sparsity

Answer

Data Currency

Question 36

In data quality assessments for big data analytics, what is the primary objective of data profiling?

Accepted Answer

Summarizing statistical properties and detecting data quality issues

Answer

Identifying and removing outliers and incorrect data values

Answer

Transforming data into an analysis-ready format

Answer

Reducing data size by eliminating duplicates and unnecessary information

Question 37

Which data scrubbing technique is specifically designed to remove duplicate and irrelevant data, leaving only the valuable and pertinent information needed for analysis?

Accepted Answer

Data Deduplication

Answer

Data Normalization

Answer

Data Imputation

Answer

Data Standardization

Question 38

Which data manipulation technique in big data analytics involves restructuring data to enhance its suitability for analysis and modeling?

Accepted Answer

Data Reshaping

Answer

Data Imputation

Answer

Data Standardization

Question 39

How does data profiling contribute to understanding data quality and characteristics during data preprocessing?

Accepted Answer

Provides insights into data distribution, patterns, and anomalies, helping identify data quality issues and characteristics.

Answer

Exclusively used to remove duplicates and handle missing values in data preprocessing.

Answer

Only applicable to small datasets due to computational costs associated with big data.

Answer

Can eliminate the need for data cleaning and transformation in data preprocessing.

Question 40

In big data analytics, how does data quality influence the analysis outcomes?

Accepted Answer

Data quality has a substantial impact on both the accuracy and reliability of the analysis results.

Answer

Data quality has no bearing on the analysis results.

Answer

Data quality solely affects the accuracy of the analysis results, not their reliability.

Question 41

In big data analytics, what are the unique challenges and complexities encountered when integrating data from diverse sources?

Accepted Answer

Data integration in big data analytics requires specialized techniques to address data heterogeneity, redundancy, and inconsistencies.

Answer

Data integration in big data analytics is simplified due to standardized data formats and schemas.

Answer

The high volume and velocity of big data make data integration less complex.

Answer

Data quality issues are less common in big data analytics because of automated data cleaning tools.

Question 42

In data preprocessing, what is the primary purpose of outlier detection and handling?

Accepted Answer

Enhance data accuracy and reliability for analysis.

Answer

Reduce data processing costs.

Answer

Maintain data consistency and prevent loss.

Answer

Improve data visualization.

Question 43

Which technique is used to identify and remove errors from big data?

Accepted Answer

Data validation

Answer

Data transformation

Answer

Data imputation

Answer

Feature engineering

Question 44

What is the process called when big data is converted into a format suitable for analysis?

Accepted Answer

Data transformation

Answer

Data cleaning

Answer

Data integration

Answer

Data reduction

Question 45

What is the difference between data validation and data transformation?

Accepted Answer

Data validation checks for errors while data transformation changes the data format.

Answer

Data transformation checks for errors while data validation changes the data format.

Answer

There is no difference between data validation and data transformation.

Answer

Data validation changes the data format while data transformation checks for errors.

Question 46

For a big data dataset with a significant number of missing values that exhibit a non-random pattern, which data imputation method is most appropriate?

Accepted Answer

Multiple Imputation by Chained Equations (MICE)

Answer

Mean imputation

Answer

Median imputation

Answer

K-Nearest Neighbors (KNN) imputation

Question 47

Which is the primary purpose of data preprocessing in big data analytics?

Accepted Answer

To convert data into a format suitable for analysis

Answer

To identify and eliminate errors from data

Answer

To handle missing data effectively

Answer

To reduce the size and complexity of data

Question 48

Which technique is commonly used to detect and remove duplicate records during data preprocessing?

Accepted Answer

Hashing

Answer

Sorting and merging

Answer

Clustering

Answer

Dimensionality reduction

Question 49

Which data transformation technique is used to convert categorical variables into numerical variables?

Accepted Answer

One-hot encoding

Answer

Principal component analysis

Answer

Normalization

Answer

Discretization

Question 50

What is the process of combining data from various sources into a single, unified dataset called?

Accepted Answer

Data integration

Answer

Data fusion

Answer

Data wrangling

Answer

Data mining

Question 51

What is the primary purpose of using normalization in data preprocessing?

Accepted Answer

To scale data attributes to a common range

Answer

To remove outliers from the data

Answer

To reduce the dimensionality of the data

Answer

To enhance the interpretability of data

Question 52

Which of the following is a potential drawback of data preprocessing in big data analytics?

Accepted Answer

Increased computational time

Answer

Reduced data accuracy

Answer

Improved data quality

Answer

Enhanced data interpretability

Question 53

What is the process of transforming data into a format suitable for analysis and modeling known as?

Accepted Answer

Data wrangling

Answer

Data mining

Answer

Data cleaning

Answer

Data integration

Question 54

Which statistical technique is used to detect outliers in big data environments, identifying data points that significantly deviate from the rest of the dataset?

Accepted Answer

Z-score method

Answer

Clustering

Answer

Regression analysis

Answer

Decision tree analysis

Question 55

What is a data dictionary primarily used for?

Accepted Answer

To describe the structure and meaning of a dataset

Answer

To visualize data

Answer

To calculate statistical measures

Answer

To implement machine learning algorithms

Question 56

What is the key distinction between data cleaning and data transformation?

Accepted Answer

Data cleaning removes errors and inconsistencies, while data transformation converts data into a suitable format

Answer

Data cleaning is a subset of data transformation

Answer

Data transformation is a subset of data cleaning

Answer

There is no difference between data cleaning and data transformation

Question 57

When preparing big data for analysis, which of these often presents a compromise?

Accepted Answer

Balancing data accuracy and completeness

Answer

Ensuring data consistency across sources

Answer

Prioritizing data transformation over error correction

Answer

Automating all data cleaning processes

Question 58

To ensure data integrity and consistency during data preprocessing and cleaning, which strategy is crucial?

Accepted Answer

Establish data governance policies and guidelines

Answer

Monitor data quality metrics

Answer

Ignore missing data points

Answer

Transform data without considering its context

Question 59

The primary objective of data preprocessing and cleaning in big data analytics is to:

Accepted Answer

Enhance the accuracy and reliability of data analysis results

Answer

Reduce the volume of big data for faster processing

Answer

Adapt data specifically for machine learning algorithms

Answer

Ensure compliance with data privacy regulations

Question 60

How does data augmentation enhance the effectiveness of data preprocessing in big data analytics?

Accepted Answer

It reduces the risk of overfitting.

Answer

It ensures data consistency.

Answer

It eliminates the need for feature engineering.

Answer

It guarantees improved model performance.

Question 61

What's the main purpose of data quality management in big data analytics?

Accepted Answer

Ensure data accuracy, validity, and reliability for reliable analysis and decision-making.

Answer

Organize data for efficient storage and retrieval.

Answer

Enhance data visualization for easy interpretation and communication.

Answer

Reduce data size for efficient storage and processing.

Question 62

Which data transformation technique converts categorical data into numerical data?

Accepted Answer

One-hot encoding

Answer

Normalization

Answer

Binning

Answer

Imputation

Question 63

What is the primary objective of data cleaning?

Accepted Answer

To rectify errors and inconsistencies

Answer

To enhance data analysis accuracy

Answer

To reduce data size

Answer

To improve data security

Question 64

Which of the following is **not** a challenge associated with big data preprocessing and cleaning?

Accepted Answer

Data consistency

Answer

Data volume

Answer

Data variety

Answer

Data speed