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4. Which one is an example of inconsistent format in data?
a. “Delhi” and “Mumbai” b. “02/04/2023” and “02-Apr-2023”

c. 45 and 100 d. 10 and ‘Ten’
5. Select the purpose for which we use Kaggle:
a. Making games b. Sharing and analysing datasets

c. Drawing images d. Creating presentations
6. Choose the scaling technique that brings all values to a range like 0 to 1.
a. Unit Scaling b. Feature Scaling (Min-Max)

c. Power Scaling d. Value Shifting
7. Which real-life example shows text standardisation?

a. “YES”, “yes”, “Y” → “Yes” b. 2 meters = 200 cm
c. 95 → A grade d. Combining two datasets
8. Select the function that is used to change data type to datetime.

a. datetime() b. strptime()
c. pd.to_datetime() d. pd.date_format()

9. Which of the following is the use of Z-score normalization.
a. To increase all values by 10
b. To convert text to lowercase

c. To make mean = 0 and standard deviation = 1
d. To draw a chart
10. Which of these tools is NOT used for data standardisation in Python?
a. StandardScaler b. MinMaxScaler

c. drop_duplicates() d. .str.lower()
11. Select the consequence of not standardising data from different sources:
a. It becomes more colorful

b. It may cause errors or confusion during analysis
c. It runs faster

d. It improves accuracy
12. Which of the following occurs when we perform the .str.lower().str.strip() function together in data
standardisation?
a. To create new columns

b. To clean and unify text entries for accurate matching
c. To count the words

d. To delete characters

B. Fill in the blanks.
1. The function converts values to categorical type.
2. allows the analysis of outliers and their impact on the data.

Theoretical and Practical Aspects of Data Processing 237