Technical interview guide

Sliding Window

A variable- or fixed-size window over a sequence, expanded and contracted in O(n) total instead of recomputing from scratch.

Read: 42 min
Practice MCQs: 25
Interview QA: 25
Edition: v6
Editorial status: Reviewed
Relevant for: AI EngineerBackend EngineerBlockchain DeveloperComputational BiologistData ScientistEmbedded Systems EngineerFrontend EngineerFull-Stack DeveloperMachine Learning EngineerQuantum Software EngineerRobotics EngineerSoftware Engineer

Scope: Language-neutral, with container costs cited from CPython 3.14 and the C++ standard containers.

Interview QA

Treat each question like a live interview question: answer out loud first (structure, assumptions, tradeoffs), then open the model answer to spot gaps and rehearse a tighter follow-up.

Curated: 2026-10-05 · Written: 2026-10-05 · Reviewed: 2026-10-03

QA-1

Explain the sliding-window idea and how it changes a production decision.

QA-2

How would you reason about fixed-size windows in a system you own?

QA-3

Walk through variable-size windows, including where engineers most often get it wrong.

QA-4

What does the window validity invariant guarantee, and what does it deliberately leave open?

QA-5

Describe monotonicity is a precondition and the evidence you would collect before relying on it.

QA-6

How do you implement the variable-size sliding window pattern to find the Longest Substring Without Repeating Characters in O(N) time?

QA-7

How do you solve Minimum Window Substring using a sliding window and frequency map, and how do you track valid match counts efficiently?

QA-8

How do you solve subarray counting problems with exact constraints, like exactly K distinct values, using sliding windows?

QA-9

How do you use a sliding window combined with a monotonic deque to solve the Sliding Window Maximum problem in O(N) time?

QA-10

How do you determine the total number of subarrays satisfying a condition using a sliding window?

QA-11

What would you measure before treating the monotonic deque for window maxima as settled?

QA-12

How does why a heap is the wrong structure here change if the workload grows by two orders of magnitude?

QA-13

A vendor promises windows over streams is handled for you. What do you still own?

QA-14

When would you refuse a design because of time-based windows?

QA-15

How would you explain the deque as the underlying container without using the usual slogan?

QA-16

What failure would you inject to check a team's understanding of windows on two dimensions?

QA-17

How should on-call treat an alert that names prefix sums as the alternative as the cause?

QA-18

What belongs in a runbook section on recording the window rather than its size, and what does not?

QA-19

How would you review a pull request whose risk is empty and degenerate windows?

QA-20

What trade-off does the amortised argument force that a junior answer usually skips?

QA-21

How would you brief product on why window state that is not a simple aggregate delays a ship date?

QA-22

What is the smallest experiment that would change your mind about windows with a shrink budget?

QA-23

How does duplicate detection within a distance interact with rollback, and where do people ignore that?

QA-24

What would you ask a candidate who recites windows in production systems but cannot apply it?

QA-25