Data Science MCQs with answers Page - 10

Here, you will find a collection of MCQ questions on Data Science. Go through these questions to enhance your preparation for upcoming examinations and interviews.

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Q. Which of the following is a common technique used to handle imbalanced datasets in classification problems?

  • (A) Upsampling
  • (B) Downsampling
  • (C) Feature Engineering
  • (D) Data Wrangling

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Q. What type of data analysis focuses on understanding the relationships and patterns within a dataset?

  • (A) Descriptive Analysis
  • (B) Predictive Analysis
  • (C) Inferential Analysis
  • (D) Diagnostic Analysis

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Q. Which of the following is NOT a key role in a typical Data Science team?

  • (A) Data Engineer
  • (B) Data Analyst
  • (C) Data Scientist
  • (D) Database Administrator

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Q. What is the process of converting categorical variables into numerical values for machine learning called?

  • (A) Feature Extraction
  • (B) Data Encoding
  • (C) Data Standardization
  • (D) Label Encoding

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Q. Which of the following is NOT a common data visualization tool or library used in Data Science?

  • (A) Tableau
  • (B) Seaborn
  • (C) Power BI
  • (D) Excel

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Q. In Data Science, what is the term for the process of reducing the dimensionality of a dataset while preserving information?

  • (A) Dimensionality Reduction
  • (B) Data Cleaning
  • (C) Feature Engineering
  • (D) Data Transformation

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Q. What is the primary goal of Data Science?

  • (A) Data Visualization
  • (B) Data Cleaning
  • (C) Predictive Analytics
  • (D) Extracting Data from APIs

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Q. Which programming language is commonly used for Data Science tasks?

  • (A) Java
  • (B) Python
  • (C) C++
  • (D) JavaScript

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Q. Which step in the Data Science process involves understanding and preparing the data for analysis?

  • (A) Data Collection
  • (B) Data Visualization
  • (C) Data Cleaning
  • (D) Model Building

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Q. What is the term for a data point that falls far from the rest of the data in a dataset?

  • (A) Outlier
  • (B) Median
  • (C) Mean
  • (D) Variance

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