A data analyst receives a list of daily sales figures as a Python list of floats. Some entries are missing (represented as None). The analyst needs to compute the average of the available values, ignoring the None entries, and return the result as a float. The analyst writes a function to do this. Which function implementation correctly handles the missing data and returns the average?

def avg_sales(sales): total = 0 count = 0 for s in sales: if s: total += s count += 1 return total / count

def avg_sales(sales): valid = [s for s in sales if s] return sum(valid) / len(valid)

def avg_sales(sales): return sum(sales) / len(sales)

def avg_sales(sales): total = 0 count = 0 for s in sales: if s is not None: total += s count += 1 return total / count

A data analyst has a dictionary mapping employee IDs to their department. They need to find the department of a specific employee, but the ID might not exist. Which code snippet safely returns the department or 'Unknown' if not found?

dept = emp_dict.get(emp_id, 'Unknown')

dept = emp_dict.setdefault(emp_id, 'Unknown')

A data analyst needs to iterate over two lists of equal length simultaneously to compute the sum of each pair. Which loop construct is most Pythonic and efficient?

for i in range(len(list1)): total = list1[i] + list2[i]

for a, b in enumerate(list1): total = a + list2[b]

for a in list1: for b in list2: total = a + b

for a, b in zip(list1, list2): total = a + b

A data analyst is writing a script that will be run daily. They want to ensure that the script's main logic only runs when the script is executed directly, not when imported as a module. Which code pattern should they use?

if __name__ == '__main__': pass

if __name__ == '__main__': main()

A data analyst is working with a large dataset in pandas. They need to filter rows where a column 'age' is greater than 30 and another column 'salary' is not null. They also need to select only the 'name' and 'salary' columns. Which code snippet is most efficient and correct?

df[(df['age'] > 30) and (df['salary'].notna())][['name', 'salary']]

df.loc[(df['age'] > 30) & (df['salary'].notna()), ['name', 'salary']]

df.query('age > 30 and salary == salary')[['name', 'salary']]

df[df['age'] > 30 & df['salary'].notna()][['name', 'salary']]

Python InstitutePCAD - Certified Associate in Data Analytics with Python

Domain 2Objective 1

Core Python Proficiency PCAD-31 Practice Questions (Page 7)

Part of the Programming and Database Skills domain, which accounts for 33.3% of the PCAD-31 exam. Python Institute does not publish an official question count, but from its 60-minute exam (~25–40 total, ~8–8 in this domain), expect 2–2 from this objective — we provide 50 practice questions to prepare you well beyond it. (estimate)

50questions here

10free pages

5concepts

33.3%of the exam

Questions 31–35

31application · medium
A data analyst receives a list of daily sales figures as a Python list of floats. Some entries are missing (represented as None). The analyst needs to compute the average of the available values, ignoring the None entries, and return the result as a float. The analyst writes a function to do this. Which function implementation correctly handles the missing data and returns the average?
Select an answer first
32application · easy
A data analyst has a dictionary mapping employee IDs to their department. They need to find the department of a specific employee, but the ID might not exist. Which code snippet safely returns the department or 'Unknown' if not found?
Select an answer first
33application · easy
A data analyst needs to iterate over two lists of equal length simultaneously to compute the sum of each pair. Which loop construct is most Pythonic and efficient?
Select an answer first
34application · easy
A data analyst is writing a script that will be run daily. They want to ensure that the script's main logic only runs when the script is executed directly, not when imported as a module. Which code pattern should they use?
Select an answer first
35expert · hard
A data analyst is working with a large dataset in pandas. They need to filter rows where a column 'age' is greater than 30 and another column 'salary' is not null. They also need to select only the 'name' and 'salary' columns. Which code snippet is most efficient and correct?
Select an answer first

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