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

1. What is the main purpose of using the bootstrapping technique in inferential statistics?

A) To filter outliers from a dataset
B) To increase the number of features in a model
C) To validate the syntax of SQL queries in Python
D) To estimate the sampling distribution by resampling with replacement

2. What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

A) Parquet supports schema evolution and columnar storage
B) Parquet stores data in plain text, making it human-readable
C) Parquet offers row-based compression
D) Parquet files can only be used with Excel

3. What is the primary goal of pre-processing data before performing analysis?

A) To minimize the number of data points
B) To ensure the data is clean, consistent, and suitable for modeling
C) To obscure sensitive information
D) To compress datasets for storage efficiency

4. Which method is appropriate for combining two DataFrames based on a shared key column where non-matching rows should be excluded from the result?

A) merge(..., how='inner')
B) join(..., how='outer')
C) append()
D) concat()

5. Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

A) Automatic plot animations
B) Changing plot color palettes
C) Adding plot titles and axis labels
D) Inserting audio annotations

Solutions: