Quiz on Handling Missing or Inconsistent Data During Data Preprocessing — Questions & Answers

This quiz contains 10 questions. Below is a complete reference of all questions, answer choices, and correct answers. You can use this section to review after taking the interactive quiz above.

1. Question 1: Types of Missing Data

   Which type of missing data occurs when the probability of missingness is related to the observed data but not to the missing data itself?

   • Missing at Random (MAR)
   • Missing Completely at Random (MCRN)
   • Missing Not at Random (MNAR)
   • Missing by Randomization (MBR)
   • Missign at Ramdon (typographical error)
   Show correct answer

   Correct answer: Missing at Random (MAR)

2. Question 2: Identifying Missing Values in Pandas

   Given the pandas DataFrame df, which code correctly counts the total number of missing values in the entire DataFrame?

   • df.isnull().sum().sum()
   • df.null().count()
   • df.missings().total()
   • df.isnull().count().sum()
   • df.hasna().sum().totl()
   Show correct answer

   Correct answer: df.isnull().sum().sum()

3. Question 3: Handling Missing Numerical Data

   What is a common and simple method for handling missing values in numerical columns during preprocessing?

   • Imputing the mean
   • Imputing with the value 'unknown'
   • Replacing with zero always
   • Duplicating nearby values
   • Remving the value entirely
   Show correct answer

   Correct answer: Imputing the mean

4. Question 4: Dropping Rows with Missing Data

   What effect does using dropna(axis=0) in pandas have on a DataFrame?

   • It removes rows containing missing values.
   • It fills missing values with zeros.
   • It removes columns containing missing values.
   • It replaces missing values with NA.
   • It converts missing values to None
   Show correct answer

   Correct answer: It removes rows containing missing values.

5. Question 5: Imputing Categorical Variables

   When handling missing values in a categorical feature, what is a common imputation strategy?

   • Filling with the mode
   • Filling with the median
   • Filling with the minimum
   • Using mean imputation
   • Replacing with NULL
   Show correct answer

   Correct answer: Filling with the mode

6. Question 6: Detecting Inconsistent Data

   If a column for gender contains entries like 'Male', 'male', 'M', and 'femal', what inconsistency does this scenario illustrate?

   • Inconsistent data representation
   • Data redundancy
   • Missing data at random
   • Irrelevant feature
   • Datta duplications (with typo)
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   Correct answer: Inconsistent data representation

7. Question 7: Using Interpolation Methods

   In time series data, which pandas method allows you to fill missing values by inferring from neighboring points?

   • interpolate()
   • insertna()
   • replace_nulls()
   • imputeAvg()
   • backfillna()
   Show correct answer

   Correct answer: interpolate()

8. Question 8: Flagging Missing Values

   Why might you want to add a binary indicator column to flag missing values before imputation?

   • To help models learn patterns involving missingness
   • To reduce the size of your dataset
   • To drop more columns in preprocessing
   • To normalize the data
   • To replace all values with the mean
   Show correct answer

   Correct answer: To help models learn patterns involving missingness

9. Question 9: Consistent Value Formatting

   Which of the following methods is effective for handling inconsistent categorical values such as 'USA', 'United States', and 'us'?

   • Standardizing values using mapping or replacement
   • Imputing missing values with the most frequent value
   • Removing duplicate records only
   • Random forest imputation
   • Sorting the DataFrame
   Show correct answer

   Correct answer: Standardizing values using mapping or replacement

10. Question 10: Removing Columns with Too Much Missing Data

    What is an appropriate action if a feature column in your dataset contains more than 80% missing values?

    • Consider dropping the column
    • Always fill missing values with zeros
    • Remove only the rows where this feature is missing
    • Impute the mean without any checks
    • Convert the column to a categorical variable
    Show correct answer

    Correct answer: Consider dropping the column