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

1. Which statements correctly describe differences between linear and logistic regression?
(Choose two)

A) Logistic regression outputs probabilities, while linear regression predicts continuous values
B) Linear regression applies only to classification tasks
C) Linear regression requires target variables to be binary
D) Logistic regression uses the logit function to model relationships

2. What is the primary reason for converting all categorical labels to lowercase during the data cleaning process?

A) To make string comparison operations slower
B) To reduce memory usage in the dataset
C) To improve data visualization aesthetics
D) To avoid treating the same category as different due to case differences

3. What is the main advantage of using Seaborn over Matplotlib when creating complex statistical visualizations in Python?

A) Seaborn works exclusively with NumPy arrays
B) Seaborn automatically saves plots as image files
C) Seaborn requires no data preprocessing for missing values
D) Seaborn provides high-level abstractions for attractive and informative graphics

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

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

5. Which features of object-oriented patterns improve clarity and reusability in Python analysis workflows?
(Choose all that apply)

A) Encapsulating behavior in class methods
B) Using global variables instead of object properties
C) Applying inheritance to avoid redundant code
D) Replacing all functions with lambda expressions

Solutions: