MongoDB·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Got a coding question from MongoDB that looked straightforward until I actually had to think about the search space. Binary search on the answer, not the index, which is a pattern I keep forgetting exists.

Questions Asked (1)

Given an integer array and an integer k, split the array into k non-empty contiguous subarrays such that the largest subarray sum is as small as possible. Return that minimum possible largest sum.

Algorithms & Data Structures

Author's notes

My first instinct was DP and I started going down that path before catching myself.

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AI HintsAI Generated

Suggested Approach

Recognize this as a binary search on the answer problem, where you search over the possible range of the largest subarray sum (from max element to total sum) and use a greedy feasibility check to determine if a given mid value is achievable with at most k splits. This approach reduces the problem from an exponential search space to O(n log(sum)) complexity. Clearly articulate why binary search applies here — the feasibility function is monotonic, making it a perfect fit.

Pro tip: Explicitly mention the monotonic property of the feasibility function: if a maximum sum X is achievable with k subarrays, then any value greater than X is also achievable. This insight is what justifies binary search and signals to the interviewer that you deeply understand why the technique applies, not just how to implement it.

1. Clarify Constraints and Edge Cases

Confirm that all integers are positive (or handle negatives), that k is between 1 and n, and ask about array size to gauge expected time complexity. Mention edge cases like k == 1 (return total sum) and k == n (return max element).

2. Define the Binary Search Bounds

Set the lower bound as the maximum single element (since every subarray must contain at least one element) and the upper bound as the total sum of the array (k=1 case). Explain why these bounds are correct and tight.

3. Implement the Greedy Feasibility Check

Write a helper function that greedily partitions the array into the minimum number of subarrays where no subarray exceeds a given limit, then checks if that count is ≤ k. Walk through the greedy logic clearly, accumulating sums and splitting when the limit would be exceeded.

4. Execute Binary Search Using the Feasibility Check

Run binary search over [lo, hi], calling the feasibility check on each midpoint. If feasible, move the upper bound down (try smaller); otherwise, move the lower bound up. Return lo when the search converges.

5. Analyze Complexity and Discuss Trade-offs

State the time complexity as O(n log(sum)) and space complexity as O(1). Briefly mention the alternative dynamic programming approach (O(n²k)) and explain why binary search is preferred for large inputs.

Key Points to Mention

Monotonic feasibility property: the decision function is monotone, which is the core justification for applying binary search on the answer
Binary search bounds: lower bound is max(array), upper bound is sum(array), and why these are the tightest valid bounds
Greedy partitioning logic: accumulate elements greedily and increment partition count when adding the next element would exceed the current limit
Time complexity O(n log(sum)) vs. the DP alternative O(n²k), and when each is preferable
Edge cases: k == 1, k == n, single-element array, and handling arrays with large values affecting the binary search range
Connection to similar problems like 'Capacity to Ship Packages Within D Days' to demonstrate pattern recognition across problem types

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