PCEP Practice Test 31 — 15 Questions

Question 1

A developer is building a simple calculator that accepts two numbers and an operator string. The code:

x = float(input("First: "))
y = float(input("Second: "))
op = input("Operator (+, -, *, /): ")
if op == "+":
    result = x + y
elif op == "-":
    result = x - y
elif op == "*":
    result = x * y
elif op == "/":
    result = x / y
print("Result:", result)

When the user enters 10, 3, and "/", the output is "Result: 3.3333333333333335". The developer wants to display only two decimal places. Which code change will achieve this without introducing errors?

Accepted Answer

Change print to: print("Result:", format(result, ".2f")). Option A is correct because the `format()` function with the format specifier `.2f` converts the floating-point result to a string rounded to two decimal places, which is exactly what the developer needs. This approach does not alter the original variable and works reliably for display purposes.

Answer

Change print to: print("Result: " + str(result)[:4])

Answer

Change print to: print("Result:", result.toFixed(2))

Answer

Change print to: print("Result:", round(result, 2))

Question 2

A company runs a script that processes user input temperatures. The script expects integers but sometimes users enter floats. The current code is: temp = int(input("Enter temperature: ")). When a user enters "36.5", the script crashes with a ValueError. The developer needs to modify the script to handle both integer and float inputs gracefully, converting the input to an integer (by truncation) for processing. Which of the following is the best course of action?

Accepted Answer

Use temp = int(float(input())). Option A is correct because it first converts the input string to a float (handling both integer and float strings), then truncates the float to an integer via int(). This gracefully processes inputs like '36.5' without crashing, meeting the requirement to truncate rather than round.

Answer

Use input with a regex check to ensure only integers are entered.

Answer

Use a try-except block to catch ValueError and ask the user to re-enter.

Answer

Use float(input()) and do not convert to int, store as float.

Question 3

A company runs a Python script on a server that monitors network traffic. The script reads a configuration file containing a threshold value as a string, e.g., '0.85'. The script compares this threshold to a calculated load average (float). The developer writes: threshold = config['threshold']; if load > threshold: alert(). The alert never triggers even when load exceeds 0.85. The config file is correctly parsed. What is the most likely cause and solution?

Accepted Answer

The threshold is a string; convert it to float before comparison.. Option B is correct because the configuration file stores the threshold as a string ('0.85'), but the comparison operator (>) in Python compares strings lexicographically, not numerically. When load is a float (e.g., 0.9), comparing a float to a string always returns False in Python 3 (raising a TypeError in some contexts, but here the comparison silently fails because the string is not converted). The fix is to explicitly convert the threshold to a float using float(config['threshold']) before the comparison.

Answer

The config file key is misspelled; check spelling.

Answer

Use a try-except block to catch TypeError.

Answer

The load variable is a string; convert to float.

Question 4

You are maintaining a legacy Python 2.7 script that calculates shipping costs. The script reads weight from user input, then calculates cost as weight * 1.5. Recently, the company upgraded to Python 3.9, and now the script raises a TypeError: can't multiply sequence by non-int of type 'float'. The input line is: weight = input('Enter weight: '). You need to fix the script minimally. Which action should you take?

Accepted Answer

Change the input line to weight = float(input('Enter weight: ')). In Python 3.9, `input()` returns a string, not a number. Multiplying a string by a float raises a TypeError. Option D converts the input to a float immediately, which is the minimal fix because it handles both integer and decimal weights correctly without changing the multiplication logic.

Answer

Use Python 2 style input by importing from __future__

Answer

Change the multiplication to cost = float(weight) * 1.5

Answer

Change the input line to weight = int(input('Enter weight: '))

Question 5

A data analyst uses Python to process a CSV file containing sales data. The file has columns: 'Product', 'Price', 'Quantity'. The analyst writes a script to compute total sales: sum of Price * Quantity for each row. The code reads each row as a list of strings. The analyst uses: total = 0; for row in reader: total += row['Price'] * row['Quantity']; print(total). The script raises a TypeError. What is the best fix?

Accepted Answer

Convert Price and Quantity to float before multiplication.. Option A is correct because the script attempts to multiply two strings (row['Price'] and row['Quantity']), which raises a TypeError. Converting both values to float before multiplication ensures numeric arithmetic. This directly addresses the type mismatch in the original code.

Answer

Change the loop to for i in range(len(reader)): total += reader[i][1] * reader[i][2].

Answer

Use integer multiplication and then convert to float.

Answer

Use float(row[1]) * float(row[2]) instead of row['Price'] * row['Quantity'].

Question 6

A junior developer is working on a script that processes user data. The script reads a CSV file into a list of dictionaries. Each dictionary represents a user with keys 'name', 'age', and 'email'. The developer needs to filter out users under 18 and store their names in a list. The current code is:

users = [{'name': 'Alice', 'age': 17, 'email': 'alice@example.com'}, {'name': 'Bob', 'age': 22, 'email': 'bob@example.com'}]

minors = []
for user in users:
    if user['age'] < 18:
        minors.append(user['name'])

print(minors)

The code works, but the senior developer says it is not idiomatic and suggests a more concise solution. Which of the following approaches is the best replacement?

Accepted Answer

minors = [user['name'] for user in users if user['age'] < 18]. Option D is correct because it uses a list comprehension to directly extract the 'name' field from each user dictionary where the age is under 18, making the code concise and Pythonic. The original code works but is verbose; list comprehensions are the idiomatic Python approach for transforming and filtering iterables in a single readable line.

Answer

minors = []
for i in range(len(users)):
    if users[i]['age'] < 18:
        minors.append(users[i]['name'])

Answer

minors = [user for user in users if user['age'] < 18]

Answer

minors = list(map(lambda u: u['name'], filter(lambda u: u['age'] < 18, users)))

Question 7

You are a data analyst at a retail company. You have a list of sales figures stored as strings in a list: sales = ['100', '200', '300']. You need to calculate the total sum. A colleague suggests using: total = sum(sales). However, this raises a TypeError because sum() requires numeric values. Which approach should you take to correctly calculate the total as an integer?

Accepted Answer

Use total = sum(int(s) for s in sales). Option B is correct because it uses a generator expression to convert each string element in the `sales` list to an integer via `int(s)` before passing the resulting integers to `sum()`. The `sum()` function requires numeric operands; passing strings directly causes a `TypeError`. By converting to integers first, the total is correctly computed as an integer.

Answer

Use total = sum(sales) and catch TypeError

Answer

Use total = 0; for s in sales: total += s

Question 8

You are an IT support specialist for a university. A professor uses a Python script that analyzes exam scores from a text file. The script calculates the average score and prints it. Recently, the script outputs 'NaN' instead of a number. The relevant code is: scores = [float(line.strip()) for line in open('scores.txt')]; average = sum(scores) / len(scores); print(average). You inspect the scores.txt file and find that one line contains the word 'Absent' and another line is blank. The professor wants the script to ignore non-numeric lines and blank lines, and also print a warning if any line was skipped. Which of the following modifications to the script best achieves this?

Accepted Answer

Open the file, iterate over lines, use try-except to convert to float, if successful append to list else increment a skip counter. At the end, print the average and the number of skipped lines.. Option A is correct: it reads line by line, tries to convert to float, catches ValueError for non-numeric lines (including 'Absent' and blank lines), skips them while counting skipped lines, and prints a warning at the end. Option B only skips blank lines but not 'Absent'. Option C uses isdigit() which fails for decimals. Option D incorrectly uses int and doesn't skip blank lines.

Answer

Read all lines, filter with a lambda that checks if line can be converted to int, then convert to float.

Answer

Use list comprehension with condition if line.strip() != '': scores = [float(line.strip()) for line in open('scores.txt') if line.strip() != '']

Answer

Check if line.strip().isdigit() before conversion, and skip if not.

Question 9

Refer to the exhibit. What is the final value of x after executing the code?

Accepted Answer

[1, 2, 3]

Answer

[1, 1, 1]

Answer

[3, 2, 1]

Answer

[3, 1, 2]

Question 10

Refer to the exhibit. What is the final value of my_list?

Accepted Answer

[1, 4, 27, 16, 125]

Answer

[1, 2, 3, 4, 5]

Answer

[1, 4, 9, 16, 25]

Answer

[1, 8, 27, 64, 125]

Question 11

You are maintaining a Python script that calculates team bonuses based on sales data. The script reads a dictionary where keys are employee names and values are total sales (float). It then applies a 10% bonus if sales exceed 5000. The code snippet is:

def calculate_bonus(sales):
    for name, value in sales.items():
        if value > 5000:
            print(f"{name} gets bonus")

However, the manager wants the script to return a list of employees who qualify, not just print them. They also want to avoid side effects. What is the best way to modify this function?

Accepted Answer

Build a list inside the function and return it at the end.. Option C is correct: collect qualifying employees in a list and return it. Option A is wrong because appending to a global list is a side effect and bad practice. Option B is wrong because it modifies the original dictionary. Option D is wrong because returning None is unhelpful.

Answer

Create a global list variable at the top of the script and append each qualifying name to it.

Answer

Keep the function as is and have the caller capture the printed names by redirecting stdout.

Answer

Use the dictionary's update method to mark bonus status in the original sales dictionary.

Question 12

You are a developer in a company that runs a Python script daily to generate reports. The script uses the os module to list files in a directory and process each. Recently, after a server migration, the script fails with 'PermissionError: [Errno 13] Permission denied'. The script runs under a service account that has read/write access to most folders, but the migration changed the permissions on certain subdirectories. The error is intermittent, occurring only for some files. You need to fix the script to continue processing other files even if one fails. Which approach should you take?

Accepted Answer

Use a try-except block inside the loop to catch PermissionError for each file and continue.. Option C is correct: embedding the file processing inside a try block and catching PermissionError specifically allows the loop to continue. Option A is wrong because checking permissions manually across all directories is error-prone and platform-dependent. Option B is wrong because ignoring all errors without distinction could mask other critical issues. Option D is wrong because running the script as a different user may solve all permission issues but is an overreaction and may not be allowed.

Answer

Wrap the entire processing loop in a try-except that catches all exceptions and passes silently.

Answer

Before processing each file, use os.access() to check permissions and skip if not accessible.

Answer

Ask the server administrator to grant full permissions to the service account on all directories.

Question 13

You are working on a Python application that interacts with an external API to fetch user data. The API returns JSON responses. Occasionally, the API returns a response with a missing key that your code assumes always exists, causing a KeyError. The application is critical and must continue functioning even if some data is incomplete. The data is processed in a loop over a list of user IDs. Your team lead suggests using the dictionary's get() method with a default value. However, the nested structure may have missing keys at multiple levels. What is the most robust way to handle this?

Accepted Answer

Use a try-except inside the loop to catch KeyError and other expected exceptions for each item.. Option D is correct: using a try-except block around the data extraction allows catching KeyError (or TypeError) and handling the error at the item level. Option A is wrong because using .get() only handles one level; nested missing keys still cause errors. Option B is wrong because checking for every key existence before access makes code verbose and still may miss some. Option C is wrong because ignoring all exceptions might hide other unrelated errors.

Answer

Use .get() with defaults for every key access, including nested ones, to avoid exceptions.

Answer

Check for key existence using 'in' for every key before accessing.

Answer

Wrap the entire loop in a try-except that catches any exception and continues.

Question 14

You are a junior developer at a logistics company. Your team maintains a Python script that processes daily shipment data from a CSV file. The script reads the file, computes total weight per shipment, and writes results to a new CSV. Recently, the script started crashing sporadically with a 'ValueError: invalid literal for int() with base 10: 'NULL''. The CSV file sometimes contains the string 'NULL' in the weight column for missing values. The current code reads the weight column as: weight = int(row['weight']). Your team lead wants a robust fix that handles missing data gracefully without crashing, and also logs the line number for any problematic rows for later review. Which of the following approaches best meets these requirements?

Accepted Answer

Wrap the int conversion in a try-except block: try: weight = int(row['weight']); except ValueError: weight = 0; log the line number using a counter variable.. Option B is correct: it uses a try-except to catch ValueError, logs the line number (using a counter), sets weight to 0, and continues processing. Option A logs but still crashes because the exception is not caught. Option C catches error but doesn't log the line number. Option D is overcomplicated and doesn't log line number explicitly.

Answer

Use the string method .isdigit(): if row['weight'].isdigit(): weight = int(row['weight']); else: weight = 0; no logging.

Answer

Read the entire file into a list, then use a list comprehension to convert weights: weights = [int(w) if w != 'NULL' else 0 for w in rows] without logging.

Answer

Add a check: if row['weight'] != 'NULL': weight = int(row['weight']); else: weight = 0; and log a warning. Do not use try-except.

Question 15

You are a junior developer at a small startup. Your team has a Python script that automates daily data processing. The script reads a CSV file, processes each row, and writes results to a new file. Recently, the script started crashing with a 'ValueError: invalid literal for int()' error. The error occurs on a line that converts a field to an integer using int() on a string value. The CSV file comes from an external source that sometimes contains non-numeric values like 'N/A' or empty strings. Which course of action is best to handle this robustly without stopping the entire process?

Accepted Answer

Wrap the conversion in a try-except block and handle the exception appropriately for each row.. Option B is correct: using a try-except block allows catching the specific error for each row and handling it (e.g., skipping or logging) while continuing the rest of the file. Option A is wrong because logging alone does not prevent the crash. Option C is wrong because replacing all values with numbers may corrupt data. Option D is wrong because having the source fix the data is not always feasible and does not solve the immediate script issue.

Answer

Add logging before the conversion to print the problematic value.

Answer

Use a regex to replace all non-digit characters before conversion.

Answer

Contact the external source to ensure no missing values are sent.