Visa·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Visa SWE interview with a tricky array problem that had me second-guessing my approach the whole time. The question sounds manageable until you actually sit with it.

Questions Asked (1)

You're given two arrays A and B of the same length. Find the longest contiguous subarray where, at each index, you can pick a value from either A or B, such that the values you pick form a non-decreasing sequence.

Algorithms & Data Structures

Author's notes

Spent the first few minutes just restating the problem back to myself which probably looked bad.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a dynamic programming solution that tracks the longest valid subarray ending at each index for both possible choices (from A or B). Optimize by maintaining the best previous values and updating in O(n) time.

Pro tip: Demonstrate awareness of trade-offs: mention that while a greedy approach might seem intuitive, it can fail because choosing a smaller value now might allow a longer sequence later; DP ensures optimality. Also, discuss how to handle ties or equal values.

1. Clarify the problem

Ask about constraints (e.g., array size, value ranges), whether the subarray must be contiguous in both arrays, and if you can switch between arrays at each index independently. Confirm that the goal is to maximize length.

2. Define DP state

Let dpA[i] be the length of the longest valid subarray ending at index i where the last picked value is A[i]. Similarly, dpB[i] for B[i]. Initialize both to 1 for each index.

3. Formulate transitions

For each i > 0, update dpA[i] = max(dpA[i], dpA[i-1] + 1 if A[i] >= A[i-1], dpB[i-1] + 1 if A[i] >= B[i-1]). Similarly for dpB[i]. This considers all valid previous choices.

4. Track maximum and optimize

Keep a running maximum of dpA[i] and dpB[i]. Since transitions only depend on the previous index, we can reduce space to O(1) by storing only the previous dp values.

5. Analyze complexity and edge cases

Time complexity is O(n) and space O(1). Discuss edge cases: n=0, n=1, all values equal, strictly increasing/decreasing arrays, and cases where switching arrays is necessary.

Key Points to Mention

Dynamic programming with two states per index (last chosen from A or B).
Transition considers all valid previous choices (from A or B) that are <= current value.
Time and space complexity: O(n) time, O(1) space with optimization.
Handling of edge cases: empty arrays, single element, equal values, and decreasing sequences.
Proof of correctness: DP ensures optimal substructure and considers all possibilities.
Comparison with greedy approach and why it fails (e.g., counterexample where greedy picks smaller value but blocks longer sequence).

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