Data Science MCQs with answers Page - 17

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Q. Which of the following function is used for searching text strings by means of regular expression?

  • (A) grepd
  • (B) grepl
  • (C) gepexpr
  • (D) all of the mentioned

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Q. What is the term for the process of reducing the dimensionality of data while retaining its most important features?

  • (A) Data Imputation
  • (B) Data Scaling
  • (C) Data Reduction
  • (D) Data Normalization

A

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Q. Which data collection method involves the collection of data at regular intervals over a period of time?

  • (A) Observational Studies
  • (B) Experiments
  • (C) Surveys
  • (D) Time Series Data Collection

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Q. What does the acronym "ETL" stand for in the context of data preprocessing and integration?

  • (A) Extract, Transform, Load
  • (B) Encrypt, Transmit, Log
  • (C) Evaluate, Test, Learn
  • (D) Estimate, Transform, Load

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Q. In data preprocessing, what is the process of converting data values into a standard format to remove noise and variations?

  • (A) Data Scaling
  • (B) Data Transformation
  • (C) Data Normalization
  • (D) Data Imputation

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Q. Which data collection method involves manipulating one or more variables to observe their effects on other variables?

  • (A) Observational Studies
  • (B) Experiments
  • (C) Surveys
  • (D) Interviews

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Q. What is the purpose of data sampling in data collection?

  • (A) To analyze the entire dataset
  • (B) To collect data from various sources
  • (C) To select a representative subset
  • (D) To remove outliers

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Q. What is the term for data that is collected from a single source at a specific point in time?

  • (A) Cross-Sectional Data
  • (B) Time Series Data
  • (C) Longitudinal Data
  • (D) Panel Data

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Q. Which data preprocessing step involves checking for and handling duplicate records in a dataset?

  • (A) Data Deduplication
  • (B) Data Aggregation
  • (C) Data Scaling
  • (D) Data Encoding

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Q. What is the primary goal of data preprocessing in the context of machine learning?

  • (A) To increase data complexity
  • (B) To prepare data for analysis
  • (C) To create visualizations of the data
  • (D) To reduce the size of the dataset

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