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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:

1. What is the main reason for using try-except blocks in a data analysis script that reads multiple files?

A) To ensure functions always return None
B) To gracefully handle runtime errors without crashing the program
C) To automatically restart the script after execution
D) To enforce strict type annotations

2. Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A) df.replace()
B) df.rename()
C) df.dropna()
D) df.sort_values()

3. What is the most efficient way to handle missing values in a Pandas DataFrame when preparing a dataset for analysis that must maintain original index structure and avoid row deletions?

A) Use fillna() with a constant value
B) Use dropna() with axis=1
C) Apply interpolate() method
D) Use drop() to remove null rows

4. Which SQL commands are typically used to retrieve and manipulate data in a data analysis context?
(Choose all that apply)
Response:

A) ROLLBACK
B) INSERT
C) SELECT
D) UPDATE

5. Which techniques can be used to select a subset of rows and columns from a DataFrame using labels?
(Choose two)

A) df['col1', 'col2']
B) df.iloc[0:5, 'col2']
C) df.loc[:, 'col1']
D) df.loc[2:7, ['col1', 'col2']]

Solutions: