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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. What is the purpose of using the groupby() function in a Pandas DataFrame when analyzing a dataset with multiple categories and numerical values?

A) To sort the DataFrame by a specific column
B) To append new rows to the DataFrame
C) To split the data into subgroups for aggregation
D) To filter rows based on index values

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

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

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

5. Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?

A) numpy.matrix()
B) numpy.outer()
C) array1 * array2
D) array1.dot(array2)

Solutions: