InterviewDB Question

Server Allocation: Assign Jobs to Servers to Minimize Maximum Load

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Question Details

Problem

You have n jobs with estimated durations and m servers. Assign each job to exactly one server to minimize the maximum total load (sum of durations) across all servers.

Return the assignment.

python
def allocate_jobs(
    jobs: list[int],   # durations
    m: int             # number of servers
) -> list[int]:        # server index for each job (0-indexed)
    pass

Example:

jobs = [3, 3, 3, 3, 3], m = 3
-> one valid assignment: [0, 1, 2, 0, 1]
   server loads: [6, 6, 3] -> max = 6

jobs = [10, 5, 5, 5], m = 2
-> [0, 1, 1, 1] -> loads: [10, 15] -> max = 15
   or [0, 0, 1, 1] -> [15, 10] -> same

Round 1 Coding

Use a greedy approach with a min-heap tracking server loads.

Follow-ups

  1. The greedy algorithm is not always optimal. Can you give a counterexample? What does the optimal solution look like?
  2. How would you use binary search + feasibility check to find the minimum possible max load?
  3. What is the relationship between this problem and the "makespan scheduling" or "bin packing" problems?
  4. If jobs can be split across servers, how does the optimal strategy change (continuous relaxation)?

Full Details

Problem

You have n jobs with estimated durations and m servers. Assign each job to exactly one server to minimize the maximum total load (sum of durations) across all servers.

Return the assignment.

python
def allocate_jobs(
    jobs: list[int],   # durations
    m: int             # number of servers
) -> list[int]:        # server index for each job (0-indexed)
    pass

Example:

jobs = [3, 3, 3, 3, 3], m = 3
-> one valid assignment: [0, 1, 2, 0, 1]
   server loads: [6, 6, 3] -> max = 6

jobs = [10, 5, 5, 5], m = 2
-> [0, 1, 1, 1] -> loads: [10, 15] -> max = 15
   or [0, 0, 1, 1] -> [15, 10] -> same

Round 1 Coding

Use a greedy approach with a min-heap tracking server loads.

Follow-ups

  1. The greedy algorithm is not always optimal. Can you give a counterexample? What does the optimal solution look like?
  2. How would you use binary search + feasibility check to find the minimum possible max load?
  3. What is the relationship between this problem and the "makespan scheduling" or "bin packing" problems?
  4. If jobs can be split across servers, how does the optimal strategy change (continuous relaxation)?
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About This Question

This is a reported interview question from a let interview during the phone round.

It covers the following topics: Heap, Phone, Binary Search, Greedy, Coding, Onsite .

Topics

Heap Phone Binary Search Greedy Coding Onsite
← All Let questions Let Software Engineer Questions → All Greedy Questions →

About Let Interview Reports

This question was reported by a candidate who interviewed at Let. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.

Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Let are the higher-signal extractions to take from this report.

For broader preparation context, the Let interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.

How To Practice This Type of Question

Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.

Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Let reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Let Round

Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.

The single most predictive failure mode in Let reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.