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

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

2. 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

3. What is one potential risk of dynamically constructing SQL queries using string concatenation in Python scripts?

A) Slower database performance
B) Missing foreign key constraints
C) Increased memory usage
D) Exposure to SQL injection attacks

4. Which practices contribute to better exception handling in data pipelines involving file I/O and numerical computations?
(Choose two)

A) Suppressing all error output using pass
B) Using specific exception types like FileNotFoundError or ZeroDivisionError
C) Catching all exceptions using a generic except: clause without specifying the error
D) Logging error messages for later inspection during failures

5. Which operations are valid when working with Pandas Series?
(Choose two)

A) Applying NumPy universal functions
B) Using .columns to rename
C) Merging by index using merge()
D) Arithmetic vector operations

Solutions: