Is Find the Largest Almost Missing Integer easy or hard?

The problem is rated Easy because the final implementation is short. The tricky part is recognizing that interior elements always belong to multiple size‑k windows, leaving only the boundary elements as candidates when 1 < k < n.

Find the Largest Almost Missing Integer Python/Java solution

Most implementations follow the same structure across languages: compute a frequency map, handle the k == 1 and k == n edge cases, then check nums[0] and nums[n-1] when 1 < k < n. The logic is simple enough to translate directly into Python dictionaries or Java HashMap implementations.

How to solve Find the Largest Almost Missing Integer in O(n)?

Count the frequency of every element using a hash map. Handle edge cases first: if k equals n, return the maximum element; if k equals 1, return the largest element with frequency 1. For 1 < k < n, only nums[0] and nums[n-1] can belong to exactly one size‑k window, so check if their frequency is 1 and return the larger valid value.

What is the best approach for Find the Largest Almost Missing Integer?

The optimal solution uses case analysis combined with a hash table for frequency counting. When 1 < k < n, only the first and last elements of the array can appear in exactly one window of size k. Count element frequencies in O(n) time and check whether nums[0] or nums[n-1] occurs exactly once. This reduces the problem to constant candidate checks after a single pass.

Is Find the Largest Almost Missing Integer asked at Google/Amazon/Meta?

This problem represents a typical array and hash table reasoning question similar to what appears in interviews at companies like Amazon, Google, and Meta. The emphasis is recognizing structural constraints of sliding windows rather than implementing complex data structures.

What data structure is used in Find the Largest Almost Missing Integer?

A hash table (hash map) is used to count the frequency of each element in the array. The frequency information helps determine whether boundary values appear exactly once, which is required for them to qualify as almost missing integers.

What is the time complexity of Find the Largest Almost Missing Integer?

The optimal algorithm runs in O(n) time with O(n) space. A single pass builds a frequency map of the array, and then constant-time checks determine whether boundary elements qualify. A naive sliding window approach would take O(n * k) time because each window requires scanning up to k elements.

Description Hints 4 Solution Video 13:21

3471. Find the Largest Almost Missing Integer

Q: Find the Largest Almost Missing Integer Python/Java solution

Most implementations follow the same structure across languages: compute a frequency map, handle the k == 1 and k == n edge cases, then check nums[0] and nums[n-1] when 1 < k < n. The logic is simple enough to translate directly into Python dictionaries or Java HashMap implementations.

Q: How to solve Find the Largest Almost Missing Integer in O(n)?

Count the frequency of every element using a hash map. Handle edge cases first: if k equals n, return the maximum element; if k equals 1, return the largest element with frequency 1. For 1 < k < n, only nums[0] and nums[n-1] can belong to exactly one size‑k window, so check if their frequency is 1 and return the larger valid value.

Q: What is the best approach for Find the Largest Almost Missing Integer?

The optimal solution uses case analysis combined with a hash table for frequency counting. When 1 < k < n, only the first and last elements of the array can appear in exactly one window of size k. Count element frequencies in O(n) time and check whether nums[0] or nums[n-1] occurs exactly once. This reduces the problem to constant candidate checks after a single pass.

Q: Is Find the Largest Almost Missing Integer asked at Google/Amazon/Meta?

This problem represents a typical array and hash table reasoning question similar to what appears in interviews at companies like Amazon, Google, and Meta. The emphasis is recognizing structural constraints of sliding windows rather than implementing complex data structures.

Q: What data structure is used in Find the Largest Almost Missing Integer?

A hash table (hash map) is used to count the frequency of each element in the array. The frequency information helps determine whether boundary values appear exactly once, which is required for them to qualify as almost missing integers.

Q: What is the time complexity of Find the Largest Almost Missing Integer?

The optimal algorithm runs in O(n) time with O(n) space. A single pass builds a frequency map of the array, and then constant-time checks determine whether boundary elements qualify. A naive sliding window approach would take O(n * k) time because each window requires scanning up to k elements.

Easy49.4% acceptance2 companies

You are given an integer array nums and an integer k.

An integer x is almost missing from nums if x appears in exactly one subarray of size k within nums.

Return the largest almost missing integer from nums. If no such integer exists, return -1.

A subarray is a contiguous sequence of elements within an array.

Example 1:

Input: nums = [3,9,2,1,7], k = 3

Output: 7

Explanation:

1 appears in 2 subarrays of size 3: [9, 2, 1] and [2, 1, 7].
2 appears in 3 subarrays of size 3: [3, 9, 2], [9, 2, 1], [2, 1, 7].
3 appears in 1 subarray of size 3: [3, 9, 2].
7 appears in 1 subarray of size 3: [2, 1, 7].
9 appears in 2 subarrays of size 3: [3, 9, 2], and [9, 2, 1].

We return 7 since it is the largest integer that appears in exactly one subarray of size k.

Example 2:

Input: nums = [3,9,7,2,1,7], k = 4

Output: 3

Explanation:

1 appears in 2 subarrays of size 4: [9, 7, 2, 1], [7, 2, 1, 7].
2 appears in 3 subarrays of size 4: [3, 9, 7, 2], [9, 7, 2, 1], [7, 2, 1, 7].
3 appears in 1 subarray of size 4: [3, 9, 7, 2].
7 appears in 3 subarrays of size 4: [3, 9, 7, 2], [9, 7, 2, 1], [7, 2, 1, 7].
9 appears in 2 subarrays of size 4: [3, 9, 7, 2], [9, 7, 2, 1].

We return 3 since it is the largest and only integer that appears in exactly one subarray of size k.

Example 3:

Input: nums = [0,0], k = 1

Output: -1

Explanation:

There is no integer that appears in only one subarray of size 1.

Constraints:

1 <= nums.length <= 50
0 <= nums[i] <= 50
1 <= k <= nums.length

Asked at 2 companies

Google Bloomberg

Topics

Array Hash Table

Hints open one at a time

Hint 1

Solve the problem for three different cases: k = 1, k = n, and 1 < k < n

Hint 2

If k = 1, return the largest element that occurs exactly once in nums

Hint 3

If k = n, return the largest element in nums

Hint 4

If 1 < k < n, all elements different from nums[0] and nums[n - 1] will occur in more than one subarray of size k. Hence, the answer is the largest of nums[0] and nums[n - 1] if they both occur exactly once in the array. If one of them occurs more than once, return the other. If both of them occur more than once, return -1.

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3471. Find the Largest Almost Missing Integer

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