Hard

Google-styleBloomberg-style

Maximum Value in Every Sliding Window DSA Interview

Given an array and a window size k, return the maximum value within every contiguous window of size k as it slides from left to right across the array.

Mock Interview with AI

1. Problem Statement

You are given an array of integers and a window size k. Return an array containing the maximum value of every contiguous window of size k as it slides from the start of the array to the end.

2. Live Coding Format

Choose Python, JavaScript, Java, or C++, then solve while explaining your reasoning to the live interviewer.

Validation uses handwritten tests only. The MVP does not compile or run code, and it does not provide AI algorithm completion.

3. Key Focus Areas

1
Problem clarification and contract
2
Approach exploration and optimality
3
Think-aloud communication and pacing
4
Implementation correctness
5
Code quality and language fluency
6
Handwritten tests and dry run
7
Debugging and self-correction
8
Complexity analysis
9
Follow-up performance

4. What Strong Candidates Should Demonstrate

✓Clarify window size bounds and output length before coding.
✓Explain a per-window rescan baseline and improve to a monotonic decreasing deque.
✓Think aloud while implementing careful deque maintenance.
✓Validate with explicit expected outputs and a top-to-bottom handwritten dry run.
✓Analyze time and space complexity and handle a focused follow-up extension.

Evaluation Guide

This is an evaluation framework, not a single model answer. Strong designs may make different choices when their assumptions and trade-offs are explicit.

Problem clarification and contract

10%

Senior signals

✓Clarifies that 1 <= k <= array length, confirms the output should have exactly (length - k + 1) values, and asks about behavior for duplicate maximums within a window.
✓Confirms whether the array can contain negative values.

Staff-level signals

◆Asks about expected array size to justify pursuing a linear-time solution.

Common red flags

×Codes immediately while assuming k is always small without confirming its relationship to array length.

Approach exploration and optimality

20%

Senior signals

✓Explains a baseline that rescans all k elements of each window to find its maximum, identifies the repeated-rescan bottleneck, and improves to a monotonic decreasing deque of indices that maintains the current window's candidates.
✓Explains why storing indices rather than values in the deque is necessary to detect when the front element has fallen out of the window.

Staff-level signals

◆Discusses why each index is pushed and popped from the deque at most once, giving the amortized linear bound.

Common red flags

×Starts implementation without explaining an approach even after one interviewer prompt.
×Proposes a heap-based approach without addressing how to remove elements that fall out of the window efficiently.

Think-aloud communication and pacing

10%

Senior signals

✓Explains the plan before coding and narrates the deque invariant (indices with strictly decreasing values) while implementing.
✓Paces the core solution to leave time for validation and the follow-up.

Common red flags

×Writes substantial code silently and cannot explain what the code is doing.

Implementation correctness

25%

Senior signals

✓Implements a correct monotonic deque that removes indices outside the current window from the front, pops smaller trailing values from the back before pushing the current index, and records the front index's value once the window is fully formed.
✓Handles k equal to array length, k equal to 1, duplicate maximum values, and a strictly decreasing or strictly increasing array.
✓Provides a convincing invariant argument for why the deque front always holds the current window's maximum.

Common red flags

×Fails to evict out-of-window indices from the front, or fails to pop dominated smaller values from the back, corrupting the invariant.
×Final code contains a material correctness bug that the candidate does not identify.

Code quality and language fluency

15%

Senior signals

✓Produces readable, language-appropriate code with a clear deque structure storing indices and well-defined eviction and push conditions.

Common red flags

×Contains off-by-one window-boundary errors or syntax-level incoherence that prevents credible reasoning.

Handwritten validation, dry run, and debugging

10%

Senior signals

✓Provides explicit inputs and expected outputs before tracing the code from top to bottom, showing deque contents after each step.
✓Covers k equal to array length, k equal to 1, duplicate maximums, and a monotonic array.
✓Finds and fixes an error independently, then re-validates the affected path.

Common red flags

×Claims the code works without a representative trace.

Complexity analysis and follow-up

10%

Senior signals

✓States amortized linear time (each index pushed and popped at most once) and auxiliary space proportional to the window size for the deque, comparing this with the per-window rescan baseline.

Staff-level signals

◆Discusses why a naive heap-based approach without lazy deletion would cost logarithmic time per element instead of amortized constant time.

Common red flags

×Cannot state the complexity of the implemented solution.
×Fails a reasonable follow-up despite having at least five minutes and receiving bounded clarification.

Trade-offs to articulate

Rescanning all k elements for every window is simple but costs O(n*k) time overall.
A monotonic decreasing deque of indices achieves amortized O(n) time since each index enters and leaves the deque at most once, using O(k) auxiliary space.
A heap-based approach can track maximums but requires lazy deletion of stale entries to handle values falling out of the window, adding complexity and a logarithmic factor.

Practice follow-up questions

1.Coding follow-up: extend the solution to also track and return the minimum value of every window simultaneously, using an analogous monotonic structure.
2.Verbal extension: how would the approach change if the window size k could vary between queries against the same array?

Want interactive feedback?

Practice this problem in the live coding workspace while the interviewer probes your clarification, algorithm, implementation, complexity, and handwritten tests.

Start Interview

Core Concepts

Monotonic StacksDequesSliding WindowComplexity Analysis