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Data Science Multiple-Choice Questions (MCQs)

Data Science is an interdisciplinary academic field [1] that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. (Read More.)

Data Science MCQs: This section contains multiple-choice questions and answers on the various topics of Data Science. Practice these MCQs to test and enhance your skills on Data Science.

List of Data Science MCQs

1. What do you mean by data science?

  1. Dealing with huge amounts of data to find marketing patterns is known as data science
  2. Extracting a meaningful insight from the data is what data science is
  3. It is a study that deals with a huge amount of all types of data structured, unstructured, or semi-structured
  4. All of the above

Answer: D) All of the above

Explanation:

Data science is the study that deals with the huge amount of all types of data i.e., structured, unstructured, and semi-structured, to find out the hidden pattern from the data which can be used for making good marketing strategies, in customer benefit, in better decision making, etc is what data science is all about.

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2. What is structured data?

  1. Structured data is a type of data that is huge in number and has many inaccurate values
  2. Structured data is a type of data that is very less in number and can be stored in proper rows and columns
  3. Structured data is a type of data that has inaccurate values but can be stored in rows and columns

Answer: B) Structured data is a type of data that is very less in number and can be stored in proper rows and columns.

Explanation:

Structured data is a type of data that is very less in number and can be stored in proper rows and columns. We can use simple MYSQL to store the structured data.

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3. What is unstructured data?

  1. Unstructured data is a type of data that is huge in number and has many inaccurate values
  2. Unstructured data is a type of data that is very less in number and can be stored in proper rows and columns
  3. Unstructured data is a type of data that has inaccurate values but can be stored in rows and columns

Answer: A) Unstructured data is a type of data that is huge in number and has many inaccurate values.

Explanation:

Unstructured data is a type of data that is huge in number and has many inaccurate values and you can not process it or store it using the traditional form of storing data.

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4. What is semi-structured data?

  1. Semi-structured data is a type of data that is huge in number and has many inaccurate values
  2. Semi-structured data is a type of data that is very less in number and can be stored in proper rows and columns
  3. Semi-structured data is a type of data that has inaccurate values but can be stored in rows and columns
  4. Semi-structured data is a type of data which has contained the data of both types i.e., structured data and semi-structured data

Answer: D) Semi-structured data is a type of data which has contained the data of both types i.e., structured data and semi-structured data.

Explanation:

Semi-structured data is a type of data which has contained the data of both types i.e., structured data and semi-structured data.

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5. What is the difference between BI (Business intelligence) and Data science?

  1. Data science deals with all types of data whereas BI deals with only structured types of data
  2. BI deals with all types of data whereas Data science deals with only structured types of data
  3. BI deals with only structured and unstructured types of data but not semi-structured whereas Data science deals with only structured types of data
  4. Data science deals with only structured and unstructured types of data but not semi-structured whereas BI deals with only structured types of data

Answer: A) Data science deals with all types of data whereas BI deals with only structured types of data.

Explanation:

Data science deals with all types of data whereas BI deals with only structured types of data.

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6. Does business intelligence focus on future predictions of data?

  1. YES
  2. NO

Answer: B) NO

Explanation:

NO, BI only deals with the past and present forms of data it has no relation to making future predictions.

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7. Which of the following are the components of data science?

  1. Statistics
  2. Data expertise
  3. Data engineering
  4. Visualization
  5. Advanced computing
  6. All of the above

Answer: D) Visualization

Explanation:

Statistics, Data expertise, Data engineering, Visualization, and Advanced computing are all components of data science.

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8. What do you mean by machine learning?

  1. ML is a branch of science that deals with data and the processing of data
  2. ML is the branch of AI (artificial intelligence) that give machines the power of what a human can do
  3. ML is the branch of AI (artificial intelligence) that only deals with computer programs to make valuable insight from the data

Answer: B) ML is the branch of AI (artificial intelligence) that give machines the power of what a human can do.

Explanation:

ML is the branch of AI (artificial intelligence) that give machines the power of what a human can do.

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9. What are the types of Machine learning?

  1. There are three types of machine learning semi-supervised, supervised, and unsupervised
  2. There are four types of machine learning semi-supervised, supervised, unsupervised, and reinforcement
  3. There are two types of machine learning supervised and unsupervised

Answer: B) There are four types of machine learning semi-supervised, supervised, unsupervised, and reinforcement.

Explanation:

There are four types of machine learning semi-supervised, supervised, unsupervised, and reinforcement.

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10. Which type of machine learning is defined by using only labeled data to predict some outcome?

  1. Semi-supervised Machine learning
  2. Unsupervised Machine learning
  3. Supervised Machine learning
  4. Reinforcement Machine learning

Answer: C) Supervised Machine learning

Explanation:

Supervised machine learning is defined as using only labeled data to predict some outcome.

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11. Which type of machine learning is defined by using only unlabelled data to analyze the data?

  1. Semi-supervised Machine learning
  2. Unsupervised Machine learning
  3. Supervised Machine learning
  4. Reinforcement Machine learning

Answer: B) Unsupervised Machine learning

Explanation:

Unsupervised machine learning is defined as using only unlabelled data to analyze the data.

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12. Which type of machine learning is defined by a combination of labeled data and unlabeled data to analyze the data?

  1. Semi-supervised Machine learning
  2. Unsupervised Machine learning
  3. Supervised Machine learning
  4. Reinforcement Machine learning

Answer: A) Semi-supervised Machine learning

Explanation:

Semi-supervised machine learning is defined by a combination of labeled data and unlabelled data to analyze the data

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13. Which type of machine learning is feedback-based machine learning?

  1. Semi-supervised Machine learning
  2. Unsupervised Machine learning
  3. Supervised Machine learning
  4. Reinforcement Machine learning

Answer: D) Reinforcement Machine learning

Explanation:

Reinforcement Machine learning is feedback-based machine learning.

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14. How many types of supervised learning are there?

  1. 2
  2. 3
  3. 4
  4. 5

Answer: A) 2

Explanation:

There are two types of supervised learning: - Classification and regression.

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15. Decision tree is a which type of machine learning algorithm?

  1. Semi-supervised Machine learning
  2. Unsupervised Machine learning
  3. Supervised Machine learning
  4. Reinforcement Machine learning

Answer: C) Supervised Machine learning

Explanation:

A decision tree is a supervised machine-learning algorithm.

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16. K- means clustering is a which type of machine learning algorithm?

  1. Semi-supervised Machine learning
  2. Unsupervised Machine learning
  3. Supervised Machine learning
  4. Reinforcement Machine learning

Answer: B) Unsupervised Machine learning

Explanation:

K- means clustering is an unsupervised machine learning algorithm.

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17. What are the four steps of data preparation?

  1. Data cleaning>Data reduction>Data transformation>Data integration
  2. Data cleaning>Data reduction> Data integration>Data transformation
  3. Data reduction> Data cleaning>Data transformation>Data integration
  4. Data cleaning> Data transformation> Data reduction>Data integration

Answer: B) Data cleaning>Data reduction> Data integration>Data transformation

Explanation:

Data cleaning>Data reduction> Data integration>Data transformation.

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18. Processing of raw data to prepare it for some other data is known as ____.

  1. Data pre-processing
  2. Data mining
  3. Data preparation
  4. Data transformation

Answer: B) Data mining

Explanation:

Pre-processing is the processing of raw data to prepare it for some other data.

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19. Which of the following are the applications of data science?

  1. Risk detection
  2. Image recognition
  3. Speech recognition
  4. All of the above

Answer: D) All of the above

Explanation:

Risk detection, Image recognition, and Speech recognition all are the application of data science.

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20. What do you mean by data mesh?

  1. A data mesh is a centralized data architecture that organizes the data according to the industry
  2. A data mesh is a decentralized data architecture that organizes the data according to the industry
  3. A data mesh is a decentralized data architecture that organizes the data and processes the data according to the industry and user needs

Answer: B) A data mesh is a decentralized data architecture that organizes the data according to the industry.

Explanation:

A data mesh is a decentralized data architecture that organizes the data according to the industry.

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21. How many types of data mesh are there?

  1. 2
  2. 4
  3. 3
  4. 5

Answer: C) 3

Explanation:

There are three types of data mesh are – file-based, event-driven, and query-enabled.

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22. How many types of data analysis are there in data science?

  1. 2
  2. 4
  3. 3
  4. 5

Answer: B) 4

Explanation:

There are four types of data analysis: - Descriptive, diagnostic, Predictive, and Prescriptive.

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23. Which type of data analysis gives a summary of the raw data set?

  1. Descriptive data analysis
  2. Diagnostic data analysis
  3. Predictive data analysis
  4. Prescriptive data analysis

Answer: A) Descriptive data analysis

Explanation:

Descriptive data analysis gives a summary of the raw data set and answers the questions like "what happened" by looking the past data.

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24. Which type of data analysis focuses on the question "Why did it happen" and finds the correlations of the causes?

  1. Descriptive data analysis
  2. Diagnostic data analysis
  3. Predictive data analysis
  4. Prescriptive data analysis

Answer: B) Diagnostic data analysis

Explanation:

Diagnostic data analysis focuses on the question "Why did it happen" and find the correlations of the causes.

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25. Which type of data analysis focuses on the question "what might happen in the future" and helps in making predictions about some sort of data?

  1. Descriptive data analysis
  2. Diagnostic data analysis
  3. Predictive data analysis
  4. Prescriptive data analysis

Answer: C) Predictive data analysis

Explanation:

Predictive data analysis focuses on the question "what might happen in the future" and helps in predicting some sort of data.

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26. Which type of data analysis focuses on the question "what should we do next" and helps in about the steps we should take to get the particular outcome?

  1. Descriptive data analysis
  2. Diagnostic data analysis
  3. Predictive data analysis
  4. Prescriptive data analysis

Answer: D) Prescriptive data analysis

Explanation:

Prescriptive data analysis focuses on the question "What should we do next" and helps in about the steps we should take to get the particular outcome.

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27. What do you mean by the model planning phase in the life cycle of data analytics?

  1. This phase involves creating data sets for training for testing, production, and training purposes
  2. This phase involves the processing of big raw data
  3. This Phase involves the team which is responsible for evaluating the tools

Answer: A) This phase involves creating data sets for training for testing, production, and training purposes.

Explanation:

The model planning phase involves creating data sets for training for testing, production, and training purposes.

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28. What are the common tools for the model planning phase?

  1. R's
  2. SQL
  3. Tableau
  4. SAS
  5. All of the above

Answer: E) All of the above

Explanation:

The common tools for the model planning phase are: - R's, SQL, Tableau, SAS, and Rapid Miner.

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29. What does GAN stand for in data science?

  1. Generative Advanced Network
  2. Generative Adversarial Network
  3. General Adversarial Network
  4. Generative Adversarial Neural

Answer: B) Generative Adversarial Network

Explanation:

GAN full form is Generative Adversarial Network.

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30. Who created GAN?

  1. Danial Smilkov
  2. Shan Carter
  3. Yann LeCun
  4. Ian J. Goodfellow

Answer: D) Ian J. Goodfellow

Explanation:

GAN was created by Ian J. Goodfellow.

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31. What is GAN?

  1. GAN is a machine learning model in which two neural networks compete to provide the most accurate and best prediction
  2. GAN is a machine learning model which is made to support a neural network
  3. GAN is a machine learning model which is used to only analyze and process the data with the help of a neural network

Answer: A) GAN is a machine learning model in which two neural networks compete to provide the most accurate and best prediction.

Explanation:

GAN is a machine learning model in which two neural networks compete to provide the most accurate and best prediction.

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32. What are the applications of GAN?

  1. Generating images
  2. Face aging
  3. Image modification
  4. All of the above

Answer: D) All of the above.

Explanation:

Generating images, Face aging, and Image modification are the applications of GAN.

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