A data analyst needs to count the frequency of each category in a list of strings. They want to use a dictionary. Which code snippet is most efficient and Pythonic?

counts = {cat: categories.count(cat) for cat in set(categories)}

from collections import Counter; counts = Counter(categories)

counts = {}; for cat in categories: counts[cat] = counts.get(cat, 0) + 1

counts = {}; for cat in categories: if cat in counts: counts[cat] += 1 else: counts[cat] = 1

A senior data analyst is reviewing a colleague's Python script that processes large log files. The script uses global variables, has no functions, and re-reads the entire file multiple times. The analyst needs to refactor it for better performance and maintainability. Which refactoring strategy is most effective?

Use pandas to read the entire file into memory and then process it.

Wrap the logic in functions, pass data as parameters, and use generators to process the file line by line.

Use multiprocessing to parallelize the script without changing its structure.

Keep the global variables but add comments to explain them.

A data analyst has a large list of dictionaries, each representing a transaction with 'date' and 'amount'. They need to find the total amount for each date, but the list is too large to fit in memory if they create a new list. Which approach is most memory-efficient?

Use a list comprehension to create a list of (date, amount) tuples and then group them.

Use a dictionary to accumulate totals while iterating over the list.

Use a set to store unique dates and then sum amounts by iterating multiple times.

Use pandas to read the data into a DataFrame and then group by date.

A data analyst is writing a function that processes a list of numbers and returns a new list with each number squared. They want to ensure the function does not modify the original list and is efficient. Which implementation is best?

def square_list(numbers): for i in range(len(numbers)): numbers[i] = numbers[i]**2; return numbers

def square_list(numbers): return list(map(lambda x: x**2, numbers))

def square_list(numbers): return [n**2 for n in numbers]

def square_list(numbers): result = []; for n in numbers: result.append(n**2); return result

A data scientist is working with a large dataset in a Jupyter notebook. They need to perform a complex aggregation that involves grouping by multiple columns and applying multiple functions. Which library and method is most suitable?

Pure Python with dictionaries and loops

pandas with groupby() and agg()

NumPy with np.unique() and np.sum()

matplotlib to visualize the aggregation

Python InstitutePCAD - Certified Associate in Data Analytics with Python

Domain 2Objective 1

Core Python Proficiency PCAD-31 Practice Questions (Page 4)

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 16–20

16application · medium
A data analyst needs to count the frequency of each category in a list of strings. They want to use a dictionary. Which code snippet is most efficient and Pythonic?
Select an answer first
17expert · hard
A senior data analyst is reviewing a colleague's Python script that processes large log files. The script uses global variables, has no functions, and re-reads the entire file multiple times. The analyst needs to refactor it for better performance and maintainability. Which refactoring strategy is most effective?
Select an answer first
18expert · hard
A data analyst has a large list of dictionaries, each representing a transaction with 'date' and 'amount'. They need to find the total amount for each date, but the list is too large to fit in memory if they create a new list. Which approach is most memory-efficient?
Select an answer first
19expert · medium
A data analyst is writing a function that processes a list of numbers and returns a new list with each number squared. They want to ensure the function does not modify the original list and is efficient. Which implementation is best?
Select an answer first
20expert · medium
A data scientist is working with a large dataset in a Jupyter notebook. They need to perform a complex aggregation that involves grouping by multiple columns and applying multiple functions. Which library and method is most suitable?
Select an answer first

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