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

1. Which practices are essential to ensure a fair evaluation of a machine learning model's performance?
(Choose two)

A) Using cross-validation to assess generalizability
B) Splitting data into training and test sets
C) Training on the entire dataset including test records
D) Randomizing the test dataset labels

2. Which of the following libraries is specifically optimized for high-performance numerical computation with multi-dimensional arrays in Python?

A) matplotlib
B) pandas
C) NumPy
D) seaborn

3. Which techniques can be used to select a subset of rows and columns from a DataFrame using labels?
(Choose two)

A) df.iloc[0:5, 'col2']
B) df.loc[2:7, ['col1', 'col2']]
C) df['col1', 'col2']
D) df.loc[:, 'col1']

4. When performing bootstrapping on a dataset with 500 observations, what is a typical procedure?

A) Creating samples by removing all duplicates
B) Scaling all values between 0 and 1 before resampling
C) Drawing one sample and calculating the mean only once
D) Generating multiple datasets of the same size by randomly sampling with replacement

5. Which approaches help tailor data visualizations for non-technical stakeholders?
(Choose two)

A) Focus on trend and summary metrics
B) Use raw code outputs for clarity
C) Add context through narrative or titles
D) Use jargon-rich annotations

Solutions: