Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Meta coding screen, pretty standard algorithmic fare. One question but it had enough follow-up angles to keep things interesting for a bit longer than I expected.

Questions Asked (1)

Given two lists a and b, return all elements from a that don't appear in b, keeping the original order from a. For example, a = [3, 1, 4, 1, 5, 9, 2] and b = [1, 5] should give [3, 4, 9, 2].

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The base case is pretty quick to code up but the follow-ups are where you can fumble.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., data types, list sizes, memory limits) and then propose a solution using a hash set for O(1) lookups, iterating through list a and checking membership in the set built from list b. Discuss time and space complexity, and consider edge cases like duplicates in a and empty lists.

Pro tip: Mention that you would use a set for b to achieve O(n + m) time complexity, but also note that if memory is a constraint, sorting b and using binary search could be an alternative with O(m log m + n log m) time and O(1) extra space (if sorting in-place is allowed). This shows you consider trade-offs.

1. Clarify requirements and constraints

Ask about input types, whether lists can be empty, if duplicates in a should be preserved, and any memory or time constraints. Confirm that the output should maintain the original order of a.

2. Propose a hash set approach

Explain that you would convert list b into a set for O(1) membership checks, then iterate through list a and collect elements not in the set. This yields O(n + m) time and O(m) space.

3. Analyze complexity and trade-offs

State the time and space complexity of the proposed solution. Discuss alternative approaches (e.g., sorting b and using binary search) and their trade-offs in terms of time, space, and simplicity.

4. Handle edge cases

Mention edge cases such as empty lists, all elements of a in b, no elements in b, and duplicates in a. Explain how the solution handles them.

5. Write clean code and test

Implement the solution in a clear, modular way, and walk through a test case (e.g., the provided example) to verify correctness.

Key Points to Mention

Use a hash set for O(1) lookups to achieve optimal time complexity.
Preserve the original order of elements from list a.
Time complexity: O(n + m) where n = len(a), m = len(b).
Space complexity: O(m) for the set, which is acceptable unless memory is constrained.
Edge cases: empty lists, duplicates in a, all elements filtered out.
Alternative approach: sort b and use binary search for O(m log m + n log m) time and O(1) extra space (if in-place sort allowed).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.