Getting Started with Data Cleaning and Preprocessing: Easy Quiz — 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: Identifying Missing Data

   When a dataset contains empty cells in the 'Age' column, which term best describes those empty values?

   • A. Missing values
   • B. Duplicated entries
   • C. Outliers
   • D. Noisy data
   • E. Meta values
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   Correct answer: A. Missing values

2. Question 2: Removing Duplicate Records

   Which data cleaning step involves deleting repeated rows, such as having the exact same customer information appear twice in a table?

   • A. Standardization
   • B. Tokenization
   • C. Removing duplicates
   • D. Clustering
   • E. Smoothing
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   Correct answer: C. Removing duplicates

3. Question 3: Understanding Outliers

   In a sales dataset, a single entry showing a sales value much higher than the others could indicate what?

   • A. Consistency
   • B. Trend
   • C. Outlier
   • D. Normalization
   • E. Null value
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   Correct answer: C. Outlier

4. Question 4: Dealing with Typos

   If a column meant to store 'Yes' or 'No' contains values like 'Ye' or 'N0', what data issue is this?

   • A. Encoding drift
   • B. Typo errors
   • C. Scaling
   • D. Feature selection
   • E. Outlier injection
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   Correct answer: B. Typo errors

5. Question 5: Data Normalization

   What is it called when you adjust numerical values to a similar range, such as converting all ages to values between 0 and 1?

   • A. Normalization
   • B. Parsing
   • C. Categorizing
   • D. Merging
   • E. Randomization
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   Correct answer: A. Normalization

6. Question 6: Categorical Data Encoding

   Transforming text labels like 'red', 'green', and 'blue' in a color column into numbers is best known as what?

   • A. Decoding
   • B. Label encoding
   • C. Aggregation
   • D. Sampling
   • E. Binning
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   Correct answer: B. Label encoding

7. Question 7: Feature Scaling Tools

   Which method can you use to ensure all features contribute equally to analysis, such as giving equal weight to 'height' in cm and 'weight' in kg?

   • A. Feature removal
   • B. Feature scaling
   • C. Data shuffling
   • D. Row expansion
   • E. Random injection
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   Correct answer: B. Feature scaling

8. Question 8: Handling Inconsistent Formats

   If some dates are formatted as '01/02/2023' and others as '2023-02-01', what type of problem does this present?

   • A. Data consistency issue
   • B. Filtering noise
   • C. Overfitting pattern
   • D. Cluster drift
   • E. Smoothing outliers
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   Correct answer: A. Data consistency issue

9. Question 9: Dealing with Noisy Data

   Suppose a sensor records temperature as 20, 21, 500, 22, 23; what is the term for unusually high or low values that may distort analysis?

   • A. Noise
   • B. Dropouts
   • C. Typecasting
   • D. Encapsulation
   • E. Nulling
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   Correct answer: A. Noise

10. Question 10: Imputing Missing Values

    If you fill empty cells in a 'salary' column with the average salary from the data, which technique are you using?

    • A. Sampling
    • B. Imputation
    • C. Redundancy removal
    • D. One-hot thresholding
    • E. Discretization
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    Correct answer: B. Imputation