← Visa Interview Insights

Visa·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Visa SWE interview with a coding question that's basically a variant of a well-known LeetCode problem, except they swap out the single parameter for three separate ones. Not a lot of context to go on from what I remember.

Questions Asked (1)

Q1

Solve a variation of a classic sliding window / dynamic programming problem where instead of one parameter k, you are given three distinct parameters that each constrain the solution differently.

Algorithms & Data Structures
Author's notes

It's basically that well-known problem but they split k into three separate values, which changes the complexity a bit.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the three parameters and how each constrains the solution, then reduce the problem to a known pattern by handling one constraint at a time. Discuss trade-offs between sliding window, dynamic programming, and hybrid approaches, and analyze time/space complexity.

Pro tip: Start by asking clarifying questions about the parameters' ranges and relationships—this shows you think about edge cases and constraints before coding. Then, relate the problem to a known variant (e.g., 3D sliding window or DP with multiple states) to demonstrate pattern recognition.

1. Clarify constraints and parameters

Ask about the meaning and typical ranges of the three parameters, and whether they interact or are independent. Confirm input/output format and edge cases.

2. Identify the core problem pattern

Determine if the problem can be modeled as a sliding window with multiple constraints, a multi-dimensional DP, or a combination. Relate it to classic problems like 'Longest Substring with At Most K Distinct Characters' but with three constraints.

3. Design an approach

Outline a strategy: e.g., use a sliding window with a data structure to track all three constraints, or use DP with state dimensions for each parameter. Discuss how to efficiently check and update constraints.

4. Analyze complexity and optimize

Derive time and space complexity. Consider optimizations like two-pointer technique, monotonic queues, or state compression if applicable.

5. Test with examples and edge cases

Walk through a small example, including cases where one parameter dominates or constraints conflict. Verify correctness and discuss potential pitfalls.

Key Points to Mention

  • Clarify the three parameters and their constraints (e.g., ranges, monotonicity, independence).
  • Relate the problem to known patterns: sliding window, dynamic programming, or hybrid approaches.
  • Discuss data structures to efficiently track multiple constraints (e.g., hash maps, heaps, segment trees).
  • Analyze time and space complexity, and compare trade-offs between different approaches.
  • Consider edge cases: empty input, parameters that make solution infeasible, or overlapping constraints.
  • Mention potential optimizations like two-pointer, monotonic queue, or state compression.

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