5. Remove Element

Q: When would you swap from the end instead?

If val is rare, swapping from the end minimizes writes. If val is common, the write-pointer pattern is better. Snowflake interviewers like the trade-off discussion.

Q: How does this connect to predicate pushdown?

When Snowflake filters WHERE col != val, the engine runs exactly this loop over each column chunk, often vectorized with SIMD. The shape of the algorithm is identical.

easyAsked at Snowflake

Remove all instances of a target value from an array in-place. Snowflake uses this to test whether you can write a tight scan loop — the same shape as a predicate pushdown filter over a column chunk.

By Alex Chen, Founder, InterviewChamp.AI · Last verified 2026-05-20

Source citations

Public interview reports confirming this problem appears in Snowflake loops.

Glassdoor (2025-Q4)— Snowflake SDE-I phone screen warm-up.
Blind (2025-12)— Reported at Snowflake intern screens.

Problem

Given an integer array nums and an integer val, remove all occurrences of val in nums in-place. The order of the elements may be changed. Then return the number of elements in nums which are not equal to val.

Constraints

0 <= nums.length <= 100
0 <= nums[i] <= 50
0 <= val <= 100

Examples

Example 1

Input

nums = [3,2,2,3], val = 3

Output

2, nums = [2,2,_,_]

Example 2

Input

nums = [0,1,2,2,3,0,4,2], val = 2

Output

5, nums = [0,1,4,0,3,_,_,_]

Approaches

1. Filter into new array

Build a new array with everything except val.

Time: O(n)
Space: O(n)

function removeElement(nums, val) {
  const result = nums.filter(x => x !== val);
  for (let i = 0; i < result.length; i++) nums[i] = result[i];
  return result.length;
}

Tradeoff: Violates the in-place requirement. Mention to acknowledge but reject.

2. Two-pointer write-index (optimal)

Walk the array. Maintain a write index that only advances when the current element is not val.

Time: O(n)
Space: O(1)

function removeElement(nums, val) {
  let write = 0;
  for (let read = 0; read < nums.length; read++) {
    if (nums[read] !== val) {
      nums[write] = nums[read];
      write++;
    }
  }
  return write;
}

Tradeoff: Single pass, O(1) extra space. Identical loop to a vectorized predicate filter.

Snowflake-specific tips

Snowflake interviewers want the read/write pointer pattern stated explicitly. Bonus signal: discuss the vectorized version — process in batches of 64 with a bitmask, the way Snowflake's execution engine actually evaluates predicates on column chunks.

Common mistakes

Using two pointers from opposite ends and swapping — works but changes order more than necessary, and complicates reasoning.
Returning the array instead of the count.
Forgetting to mutate nums (just incrementing a counter doesn't change the array).

Follow-up questions

An interviewer at Snowflake may pivot to one of these next:

What if you must preserve the original order of remaining elements?
What if val could be a list of values?
Vectorize this — process 64 elements at a time with a bitmask.

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Output

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FAQ

When would you swap from the end instead?

If val is rare, swapping from the end minimizes writes. If val is common, the write-pointer pattern is better. Snowflake interviewers like the trade-off discussion.

How does this connect to predicate pushdown?

When Snowflake filters WHERE col != val, the engine runs exactly this loop over each column chunk, often vectorized with SIMD. The shape of the algorithm is identical.

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