Data Preparation and Preprocessing for Cloud AI and ML: Master Data Prep

Question 1

In data preprocessing, which technique is used to handle missing values?

Accepted Answer

Imputation

Answer

Normalization

Answer

One-hot encoding

Answer

Feature scaling

Question 2

Which data transformation technique converts categorical data into numerical values?

Accepted Answer

One-hot encoding

Answer

Normalization

Answer

Imputation

Answer

Outlier removal

Question 3

Which of the following is NOT a task performed during data preprocessing?

Accepted Answer

Model training

Answer

Data transformation

Answer

Feature extraction

Answer

Data cleaning

Question 4

What is the primary goal of data cleaning?

Accepted Answer

To remove errors, inconsistencies, and duplicate data to improve data quality.

Answer

To transform data into a format suitable for modeling.

Answer

To select the most relevant features for analysis.

Question 5

Which data transformation technique is commonly used to encode categorical variables with binary values?

Accepted Answer

One-hot encoding

Answer

Normalization

Answer

Imputation

Answer

Discretization

Question 6

What is the key distinction between feature selection and feature engineering?

Accepted Answer

Feature selection identifies existing relevant features, while feature engineering creates new ones to enhance model performance.

Answer

Feature selection reduces the number of features, while feature engineering increases it.

Answer

Feature selection is performed before modeling, and feature engineering is performed after modeling.

Question 7

Which metric is commonly used to assess the completeness of a cleaned dataset?

Accepted Answer

Percentage of missing values

Answer

F1-score

Answer

Accuracy

Answer

Precision

Question 8

How do missing values impact the performance of machine learning models?

Accepted Answer

They can introduce bias, reduce model accuracy, and affect interpretability.

Answer

They only affect models that require complete data.

Answer

They always improve model performance.

Question 9

Which technique is commonly used to address extreme values (outliers) in data?

Accepted Answer

Capping or winsorization

Answer

Feature selection

Answer

Imputation

Answer

Transformation

Question 10

What is the purpose of normalization in data preprocessing?

Accepted Answer

To transform data to a common scale, improving comparability and model performance.

Answer

To transform categorical data into numerical data.

Answer

To create new features from existing ones.

Answer

To remove duplicate data.

Question 11

The technique used to identify and address missing values in a dataset is:

Accepted Answer

Imputation

Answer

Feature normalization

Answer

Outlier detection

Answer

Dimensionality reduction

Question 12

Feature scaling is a data preprocessing technique used to:

Accepted Answer

Ensure all features are on the same scale, improving model performance and interpretability.

Answer

Detect and remove outliers from a dataset.

Answer

Reduce the number of features in a dataset.

Answer

Handle missing values by replacing them with statistical estimates.

Question 13

Which feature selection approach evaluates the correlation between features and the target variable to identify redundant or irrelevant features?

Accepted Answer

Filter method

Answer

Embedded method

Answer

Wrapper method

Answer

Regularization

Question 14

The goal of data transformation in preprocessing is to:

Accepted Answer

Enhance data quality, consistency, and compatibility for improved modeling capabilities.

Answer

Make the data more human-readable and understandable.

Answer

Automate the data analysis process and replace human intervention.

Answer

Reduce the size of the dataset and optimize storage requirements.

Question 15

Which preprocessing technique is commonly used to handle extreme values (outliers) in a dataset?

Accepted Answer

Capping or Winsorization

Answer

Feature scaling

Answer

Imputation

Answer

Data discretization

Question 16

Data partitioning in preprocessing plays a significant role in:

Accepted Answer

Dividing the dataset into distinct subsets (training, validation, and testing) for model development and evaluation.

Answer

Ensuring data integrity and preventing data loss.

Answer

Reducing computational complexity and improving efficiency.

Question 17

Dimensionality reduction in preprocessing offers several benefits, including:

Accepted Answer

Reducing model complexity, improving interpretability, and potentially enhancing performance.

Answer

Increasing data storage capacity and reducing computational requirements.

Answer

Improving data quality and addressing missing values.

Answer

Automating the feature engineering process and eliminating manual intervention.

Question 18

Data normalization in preprocessing serves the primary purpose of:

Accepted Answer

Transforming data into a consistent format, often by scaling or centering the features.

Answer

Reducing the dimensionality of the dataset by removing redundant features.

Answer

Handling missing values and imputing them with plausible estimates.

Answer

Identifying and resolving data inconsistencies and errors.

Question 19

A preprocessing pipeline offers advantages such as:

Accepted Answer

Automating data preparation tasks, ensuring reproducibility, and providing a structured approach to preprocessing.

Answer

Reducing model accuracy and increasing training time.

Answer

Limiting the flexibility of data exploration and analysis.

Question 20

In AI and ML, data engineering primarily focuses on:

Accepted Answer

Preparing and transforming raw data into a format suitable for modeling and analysis.

Answer

Developing and deploying machine learning algorithms.

Answer

Visualizing and interpreting data insights.

Question 21

Which of the following is a crucial step in the data preprocessing workflow for ML models?

Accepted Answer

Data Cleaning

Answer

Feature Extraction

Answer

Model Tuning

Answer

Model Deployment

Question 22

What is the primary objective of data normalization?

Accepted Answer

Convert data values to a consistent scale.

Answer

Encode categorical data into numerical data.

Answer

Remove duplicate data points.

Answer

Reduce the dimensionality of the dataset.

Question 23

Which technique is commonly employed to address missing numerical data?

Accepted Answer

Imputation

Answer

Dummy Variable Creation

Answer

One-Hot Encoding

Answer

Quantization

Question 24

What is the aim of feature scaling?

Accepted Answer

Transform features to a common range.

Answer

Reduce the influence of outliers.

Answer

Make features more interpretable.

Answer

Identify highly correlated features.

Question 25

What is the primary reason for removing outliers from a dataset before modeling?

Accepted Answer

Enhance model performance and accuracy.

Answer

Ensure data consistency.

Answer

Make the data distribution more normal.

Answer

Reduce the variance of the data.

Question 26

Which of the following is NOT a fundamental step commonly found in the data preprocessing workflow?

Accepted Answer

Data summarization

Answer

Data transformation

Answer

Data cleaning

Answer

Data integration

Question 27

What is the primary goal of data cleaning in data preprocessing?

Accepted Answer

To remove errors, inconsistencies, and duplicate data, improving its quality.

Answer

To transform data into a format suitable for modeling.

Answer

To reduce the dimensionality of the data.

Question 28

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

Accepted Answer

One-hot encoding

Answer

Standardization

Answer

Normalization

Answer

Binning

Question 29

What is a key benefit of applying feature scaling in data preprocessing?

Accepted Answer

It improves the performance and convergence of machine learning algorithms.

Answer

Increases the interpretability of the model.

Answer

Reduces the dimensionality of the data.

Question 30

Which of the following is a widely used data engineering tool for data preprocessing tasks?

Accepted Answer

Apache Spark

Answer

Google Analytics

Answer

Tableau

Answer

Power BI

Question 31

What is a key distinction between data cleaning and data wrangling?

Accepted Answer

Data wrangling encompasses a broader range of data management tasks beyond cleaning, including transformation and integration.

Answer

Data cleaning focuses on removing errors, while data wrangling prepares data for modeling.

Answer

Data wrangling is a subset of data cleaning.

Question 32

Which of the following is a recommended practice for handling missing data in data preprocessing?

Accepted Answer

Impute the missing values using a suitable statistical method.

Answer

Delete the records with missing values.

Answer

Replace the missing values with the mean value of the column.

Question 33

What is the primary objective of exploratory data analysis (EDA) in data preprocessing?

Accepted Answer

To gain insights into the structure, distribution, and relationships within the data, informing subsequent preprocessing steps.

Answer

To transform the data into a suitable format for modeling.

Answer

To identify and fix errors in the data.

Question 34

Which cloud service is specifically designed for data preprocessing and data engineering tasks?

Accepted Answer

Google Cloud Dataprep

Answer

Google Cloud BigQuery

Answer

Google Cloud Storage

Answer

Google Cloud ML Engine

Question 35

Why is data preprocessing essential for cloud AI and ML applications?

Accepted Answer

It improves the accuracy, efficiency, and interpretability of machine learning models, enabling them to learn from data effectively.

Answer

It ensures compliance with data privacy regulations.

Answer

It reduces the computational cost of training machine learning models.

Question 36

Which of the following is NOT a typical step in data preparation and preprocessing for machine learning?

Accepted Answer

Data Encryption

Answer

Data Transformation

Answer

Data Cleaning

Answer

Feature Engineering

Question 37

What is the main purpose of data normalization in machine learning?

Accepted Answer

To bring features to a similar scale, often improving model performance.

Answer

To convert categorical variables into numerical values.

Answer

To remove outliers from the dataset.

Question 38

Which data transformation technique converts categorical features into numerical ones?

Accepted Answer

One-Hot Encoding

Answer

Log Transformation

Answer

Standardization

Answer

Binning

Question 39

You have a dataset with a skewed distribution. Which transformation technique is most likely to help address this?

Accepted Answer

Log Transformation

Answer

Principal Component Analysis (PCA)

Answer

One-Hot Encoding

Answer

Min-Max Scaling

Question 40

What is the primary objective of feature engineering in machine learning?

Accepted Answer

To create new, potentially more informative features from existing ones, aiming to improve model performance.

Answer

To handle missing values in the dataset.

Answer

To reduce the dimensionality of the dataset.

Question 41

Which of these is NOT a typical feature engineering technique?

Accepted Answer

Data Encryption

Answer

Domain Knowledge-based Features

Answer

Interaction Features

Answer

Polynomial Features

Question 42

You have a dataset with highly correlated features. Which technique can help reduce dimensionality and potentially improve model performance?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Binning

Answer

Log Transformation

Answer

One-Hot Encoding

Question 43

What does data cleaning involve in the context of machine learning?

Accepted Answer

Identifying and removing inconsistent or erroneous data points from the dataset.

Answer

Transforming data into a different format or representation.

Answer

Exploring and visualizing the data to gain insights.

Answer

Creating new features from existing ones.

Question 44

Why is data preparation and preprocessing essential for building effective machine learning models?

Accepted Answer

It improves the quality and performance of the model by addressing data issues that can hinder learning.

Answer

It reduces the computational cost of training the model.

Question 45

Which of the following is NOT a common data preprocessing technique?

Accepted Answer

Data Aggregation

Answer

Data Cleaning

Answer

Feature Engineering

Answer

Data Transformation

Question 46

What is the primary purpose of data cleaning in machine learning?

Accepted Answer

To improve the accuracy and reliability of machine learning models.

Answer

To create new features for the model.

Answer

To reduce the size of the dataset.

Question 47

Which data cleaning technique would you use to handle missing values in a dataset?

Accepted Answer

Imputation

Answer

Dimensionality Reduction

Answer

Normalization

Answer

Feature Scaling

Question 48

You are working with a dataset containing income data in different currencies. Which data transformation technique would be most appropriate?

Accepted Answer

Currency Conversion

Answer

Binning

Answer

Standardization

Answer

One-Hot Encoding

Question 49

What is the main goal of feature engineering in machine learning?

Accepted Answer

To create new features that improve the performance of the model.

Answer

To normalize the feature values.

Answer

To reduce the number of features in the dataset.

Question 50

Which of the following is a common technique for handling categorical variables in machine learning?

Accepted Answer

One-Hot Encoding

Answer

Principal Component Analysis (PCA)

Answer

Feature Scaling

Answer

Mean Imputation

Question 51

You have a dataset with highly skewed feature values. Which technique would you use to address this issue?

Accepted Answer

Log Transformation

Answer

Standardization

Answer

Dimensionality Reduction

Answer

Binning

Question 52

What is the purpose of data normalization in machine learning?

Accepted Answer

To ensure that features have a similar scale, preventing certain features from dominating others.

Answer

To reduce the number of features in the dataset.

Answer

To convert categorical variables into numerical ones.

Question 53

You are building a machine learning model to predict customer churn. What kind of data preprocessing step might be crucial in this scenario?

Accepted Answer

Feature Engineering to create new features that capture customer behavior patterns.

Answer

Removing outliers to improve the model's accuracy.

Answer

Data Standardization to ensure all features are on the same scale.

Question 54

Which of the following is NOT a benefit of using a cloud-based platform for data preprocessing?

Accepted Answer

Reduced computational cost compared to on-premise solutions.

Answer

Scalability to handle large datasets.

Answer

Access to a wide range of tools and services.