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

1. Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?

A) To evaluate the model's performance on unseen data
B) To optimize the model's training accuracy
C) To reduce the number of iterations during training
D) To ensure the loss function is minimized

2. Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)

A) Resetting the index after filtering
B) Dropping column headers
C) Converting the DataFrame to a Series
D) Ensuring consistent data types

3. Which method in the pandas library allows efficient loading of large datasets in chunks to conserve memory?

A) read_csv(chunksize=...)
B) fetch_chunks()
C) import_csv()
D) load_dataset()

4. What is a key assumption of linear regression that distinguishes it from logistic regression?

A) Output values must be binary
B) The relationship between predictors and the outcome is linear
C) The target variable is unordered categorical
D) Predictors must be independent

5. Which techniques are commonly used to manage type conversion between SQL and Python when importing database values?
(Choose two)

A) Defining column types in Python using sqlite3.register_converter()
B) Using a mapping function to convert SQL types into Python objects
C) Automatically casting Python variables to SQL column types without validation
D) Ignoring data types and treating everything as strings

Solutions: