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Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Meta coding screen for a software engineer role, one question the whole time, pretty focused on the data structure angle rather than just getting a working solution.

Questions Asked (1)

Q1

You have two arrays and a sequence of operations: one operation type updates a value in the second array at a given index, the other asks how many index pairs (i, j) satisfy primary[i] + secondary[j] == some target. Return results for all query operations in order. How do you handle this efficiently?

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

My first instinct was brute force the pair count each time, which is obviously bad.

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AI HintsAI Generated

Suggested Approach

Clarify the constraints and operation mix, then propose a hybrid approach: preprocess the static array into a frequency map and handle updates to the dynamic array by maintaining a frequency map that is updated in O(1). For each query, iterate over the smaller frequency map and look up the complement in the other, achieving O(min(distinct values)) per query. Discuss trade-offs and mention that if updates are frequent and queries are rare, a different strategy like recomputing might be better.

Pro tip: Always start by asking about the relative frequency of updates vs. queries and the value ranges; this shows you think about real-world performance and can lead to a more tailored solution. Also, mention that using a hash map for frequencies is often faster than sorting when values are sparse or updates are frequent.

1. Clarify constraints and requirements

Ask about array sizes, number of operations, value ranges, and whether updates and queries are interleaved. This determines the optimal data structures and algorithm.

2. Choose data structures

Use frequency maps (hash maps) for both arrays to allow O(1) updates and fast lookups. Alternatively, if the static array is large and queries are many, consider sorting it and using binary search, but updates to the dynamic array would still require a frequency map.

3. Design the query algorithm

For each query, iterate over the distinct values of the smaller frequency map, compute the complement (target - value), and add the product of frequencies if the complement exists in the other map. This yields O(min(distinct values)) per query.

4. Handle updates efficiently

When an update occurs, decrement the frequency of the old value and increment the frequency of the new value in the dynamic array's frequency map. This is O(1) per update.

5. Analyze complexity and trade-offs

State the time complexity: O(1) per update, O(min(distinct values)) per query. Discuss alternatives like recomputing from scratch if updates are very frequent and queries are rare, or using a Fenwick tree if values are bounded and we need to count pairs with sum target.

Key Points to Mention

  • Frequency maps (hash maps) for O(1) updates and lookups
  • Iterating over the smaller frequency map to minimize query time
  • Handling duplicates by storing frequencies, not just presence
  • Time complexity analysis: O(1) update, O(min(distinct)) query
  • Trade-offs between different approaches based on operation mix
  • Edge cases: empty arrays, target not present, large value ranges

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