Sliding window algorithm in Rest API - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of sliding window sum calculation

What is the output of this REST API response when calculating the sum of every 3 consecutive numbers in the list?

Rest API

numbers = [1, 3, 5, 7, 9]
window_size = 3
result = []
for i in range(len(numbers) - window_size + 1):
    window_sum = sum(numbers[i:i+window_size])
    result.append(window_sum)
print(result)

A [3, 5, 7]

B [1, 3, 5, 7, 9]

C [15, 21, 27]

D [9, 15, 21]

💡 Hint

Think about how many sums you get when sliding a window of size 3 over 5 numbers.

The sliding window sums are: 1+3+5=9, 3+5+7=15, 5+7+9=21. So the output list is [9, 15, 21].

🧠 Conceptual

intermediate

Sliding window algorithm main advantage

What is the main advantage of using a sliding window algorithm over a naive approach when processing sequences?

A It reduces time complexity by avoiding repeated calculations.

B It uses more memory to store all intermediate results.

C It always produces exact results without approximation.

D It requires sorting the input data first.

💡 Hint

Think about how sliding windows reuse previous computations.

Sliding window algorithms reuse previous computations to avoid recalculating sums or other metrics, reducing time complexity.

🔧 Debug

advanced

Identify the error in sliding window max calculation

This code tries to find the maximum value in every sliding window of size 2 but produces wrong output. What is the error?

Rest API

nums = [4, 2, 12, 3]
window = 2
max_values = []
for i in range(len(nums) - window):
    max_values.append(max(nums[i:i+window]))
print(max_values)

A The window size variable is not defined before the loop.

B The max function is used incorrectly; it should be min instead.

C The loop range should be len(nums) - window + 1 to include the last window.

D The slice nums[i:i+window] should be nums[i:i+window-1].

💡 Hint

Check how many windows the loop covers compared to the input length.

The loop stops one index too early because it uses len(nums) - window instead of len(nums) - window + 1, missing the last window.

📝 Syntax

advanced

Syntax error in sliding window average calculation

Which option contains the correct syntax to calculate the average of every sliding window of size 4 in a list?

Rest API

data = [10, 20, 30, 40, 50, 60]
window_size = 4
averages = []
for i in range(len(data) - window_size + 1):
    avg = sum(data[i:i+window_size]) / window_size
    averages.append(avg)
print(averages)

A

for i in range(len(data) - window_size + 1): avg = sum(data[i:i+window_size]) / window_size
averages.append(avg)

B for i in range(len(data) - window_size + 1): avg = sum(data[i:i+window_size]) / window_size; averages.append(avg)

C )gva(dneppa.segareva ezis_wodniw / )]ezis_wodniw+i:i[atad(mus = gva :)1 + ezis_wodniw - )atad(nel(egnar ni i rof

D for i in range(len(data) - window_size + 1): avg = sum(data[i:i+window_size]) / window_size averages.append(avg)

💡 Hint

Check for proper line breaks or statement separators in Python loops.

In Python, each statement inside a loop must be on its own line or separated by a semicolon. Option A correctly uses a newline.

🚀 Application

expert

Sliding window for longest substring without repeating characters

Given the string 'abcabcbb', what is the length of the longest substring without repeating characters using a sliding window approach?

A 4

B 3

C 2

D 5

💡 Hint

Try to find the longest substring where no character repeats by moving the window.

The longest substring without repeating characters is 'abc' with length 3.