Sliding Window on Arrays in DSA Javascript - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to slide the window by moving the start index forward.

DSA Javascript

let start = 0;
let end = 3;

while (end < arr.length) {
  // process window arr[start..end]
  start = start [1] 1;
  end++;
}

Drag options to blanks, or click blank then click option'

A+

B-

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using '-' will move the window backward, causing errors.

To move the window forward, we increase the start index by 1 using '+'.

2fill in blank

medium

Complete the code to calculate the sum of the current sliding window.

DSA Javascript

let windowSum = 0;
for (let i = start; i [1] end; i++) {
  windowSum += arr[i];
}

Drag options to blanks, or click blank then click option'

A<=

B>=

C<

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' excludes the last element of the window.

The loop should include the end index, so use '<=' to cover all elements in the window.

3fill in blank

hard

Fix the error in updating the window sum when sliding the window forward by one element.

DSA Javascript

windowSum = windowSum - arr[start] [1] arr[end + 1];

Drag options to blanks, or click blank then click option'

A/

B-

C*

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using '-' will subtract the new element, causing incorrect sums.

When sliding the window, subtract the element leaving and add the new element entering the window using '+'.

4fill in blank

hard

Fill both blanks to create a sliding window that finds the maximum sum of any window of size k.

DSA Javascript

let maxSum = 0;
let windowSum = 0;

for (let i = 0; i < k; i++) {
  windowSum += arr[i];
}
maxSum = windowSum;

for (let i = k; i < arr.length; i++) {
  windowSum = windowSum [1] arr[i - k] [2] arr[i];
  if (windowSum > maxSum) {
    maxSum = windowSum;
  }
}

Drag options to blanks, or click blank then click option'

A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' for both will add too much.

Using '-' for both will subtract too much.

To slide the window, subtract the element leaving (arr[i - k]) and add the new element entering (arr[i]).

5fill in blank

hard

Fill all three blanks to create a sliding window that finds the minimum length subarray with sum at least target.

DSA Javascript

let minLength = Infinity;
let start = 0;
let currentSum = 0;

for (let end = 0; end < arr.length; end++) {
  currentSum += arr[end];
  while (currentSum [1] target) {
    minLength = Math.min(minLength, end [2] start [3] 1);
    currentSum -= arr[start];
    start++;
  }
}

Drag options to blanks, or click blank then click option'

A>=

B-

C+

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' instead of '>=' in the while condition.

Calculating length incorrectly.

The loop shrinks the window while currentSum is greater or equal to target. The length is end - start + 1.

Practice

1. What is the main purpose of the sliding window technique when working with arrays?

easy

A. To find the maximum element in the entire array

B. To sort the array elements in ascending order

C. To reverse the array elements in place

D. To efficiently process subarrays of fixed size without recomputing everything

Solution

1. Step 1: Understand the sliding window concept

   The sliding window technique moves a fixed-size window over the array to process subarrays efficiently.

2. Step 2: Compare with other options

   Sorting, reversing, or finding max in the entire array are different operations not related to sliding window.

3. Final Answer:

   To efficiently process subarrays of fixed size without recomputing everything -> Option D

4. Quick Check:

   Sliding window = efficient fixed-size subarray processing [OK]

Hint: Sliding window moves fixed-size subarray to save work [OK]

Common Mistakes:

• Confusing sliding window with sorting or reversing
• Thinking it processes the whole array at once
• Assuming it finds max without window size

2. Which of the following is the correct syntax to initialize a sliding window sum for the first k elements in JavaScript?

easy

A. let sum = arr.slice(0, k).reduce((a, b) => a + b, 0);

B. let sum = arr.reduce((a, b) => a + b, 0);

C. let sum = arr.slice(k).reduce((a, b) => a + b, 0);

D. let sum = arr.splice(0, k).reduce((a, b) => a + b, 0);

Solution

1. Step 1: Understand how to get first k elements

   Using arr.slice(0, k) extracts the first k elements without modifying the array.

2. Step 2: Sum the elements correctly

   Using reduce((a, b) => a + b, 0) sums all elements in the sliced array.

3. Final Answer:

   let sum = arr.slice(0, k).reduce((a, b) => a + b, 0); -> Option A

4. Quick Check:

   slice(0,k) + reduce sum = correct initialization [OK]

Hint: Use slice(0,k) to get first window, then reduce to sum [OK]

Common Mistakes:

• Using reduce on whole array instead of first k elements
• Using splice which modifies the original array
• Slicing from k to end instead of 0 to k

3. What will be the output of the following code?

const arr = [1, 3, 2, 6, -1, 4, 1, 8, 2];
const k = 5;
let maxSum = 0;
let windowSum = 0;

// Initialize first window sum
for(let i = 0; i < k; i++) {
  windowSum += arr[i];
}
maxSum = windowSum;

// Slide the window
for(let i = k; i < arr.length; i++) {
  windowSum += arr[i] - arr[i - k];
  if(windowSum > maxSum) maxSum = windowSum;
}
console.log(maxSum);

medium

A. 18

B. 17

C. 16

D. 19

Solution

1. Step 1: Calculate initial window sum for first 5 elements

   Sum of [1,3,2,6,-1] = 1+3+2+6-1 = 11

2. Step 2: Slide window and update sums

   Next windows and sums:
   Remove 1, add 4: 11 - 1 + 4 = 14
   Remove 3, add 1: 14 - 3 + 1 = 12
   Remove 2, add 8: 12 - 2 + 8 = 18 (maxSum updated to 18)
   Remove 6, add 2: 18 - 6 + 2 = 14

3. Step 3: Find maxSum

   Max sum found is 18

4. Final Answer:

   18 -> Option A

5. Quick Check:

   Max sliding window sum = 18 [OK]

Hint: Update sum by subtracting left, adding right element [OK]

Common Mistakes:

• Not updating maxSum correctly
• Adding instead of subtracting old element
• Wrong initial sum calculation

4. Identify the bug in the following sliding window code that finds the maximum sum of subarrays of size k:

function maxSlidingWindowSum(arr, k) {
  let maxSum = 0;
  let windowSum = 0;
  for(let i = 0; i <= k; i++) {
    windowSum += arr[i];
  }
  maxSum = windowSum;
  for(let i = k; i < arr.length; i++) {
    windowSum += arr[i] - arr[i - k];
    if(windowSum > maxSum) maxSum = windowSum;
  }
  return maxSum;
}

medium

A. The subtraction in the second loop is incorrect

B. The first loop should run while i < k, not i <= k

C. maxSum should be initialized to Number.MIN_VALUE

D. The function does not handle empty arrays

Solution

1. Step 1: Check the first loop range

   The first loop runs from i = 0 to i <= k, which means it accesses arr[k], an out-of-bound index for window size k.

2. Step 2: Correct the loop condition

   The loop should run while i < k to sum exactly k elements starting from index 0.

3. Final Answer:

   The first loop should run while i < k, not i <= k -> Option B

4. Quick Check:

   Loop range for first window = i < k [OK]

Hint: Loop for first window sums exactly k elements: i < k [OK]

Common Mistakes:

• Using <= k causes out-of-bounds error
• Ignoring array length checks
• Incorrect initialization of maxSum

5. Given an array of integers and a number k, how would you find the length of the longest subarray with sum less than or equal to k using the sliding window technique?

hard

A. Calculate prefix sums and find max subarray sum using Kadane's algorithm

B. Sort the array and use binary search to find subarrays with sum ≤ k

C. Expand the window by moving right pointer and shrink from left when sum exceeds k, track max length

D. Use nested loops to check all subarrays and pick the longest with sum ≤ k

Solution

1. Step 1: Use two pointers to create a sliding window

   Start with both pointers at the beginning, expand right pointer to add elements to sum.

2. Step 2: Shrink window from left when sum exceeds k

   If sum > k, move left pointer forward to reduce sum until sum ≤ k again, tracking max window length.

3. Step 3: Track the maximum length of valid windows

   Keep updating max length whenever sum ≤ k during expansion and shrinking.

4. Final Answer:

   Expand the window by moving right pointer and shrink from left when sum exceeds k, track max length -> Option C

5. Quick Check:

   Sliding window with two pointers and sum check = Expand the window by moving right pointer and shrink from left when sum exceeds k, track max length [OK]

Hint: Move right to expand, left to shrink when sum > k [OK]

Common Mistakes:

• Using sorting or binary search which doesn't apply here
• Using nested loops causing O(n²) time
• Confusing Kadane's algorithm with sum limit problem