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

1. Which two benefits are most closely associated with using object-oriented programming (OOP) in data analysis pipelines?
(Choose two)

A) Encourages code reuse through inheritance and modularity
B) Supports clearer abstraction by modeling real-world entities
C) Enables procedural programming only through decorators
D) Allows the use of lambda for procedural logic

2. Which file formats are commonly used for hierarchical or nested data structures in data acquisition?
(Choose two)

A) CSV
B) JSON
C) TSV
D) XML

3. Which technique would be most appropriate to handle missing numerical values in a dataset intended for machine learning?

A) Filling with random values
B) Dropping all columns
C) Imputation using mean or median
D) Replacing with NULL

4. What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?

A) .loc[] can only be used on Series
B) .iloc[] allows label-based selection
C) .loc[] does not allow slicing
D) .iloc[] is position-based, while .loc[] is label-based

5. Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)

A) Removing rows with invalid labels
B) Replacing inconsistent labels with a standardized value
C) Converting all values to integers
D) Normalizing using min-max scaling

Solutions: