← Mistral AI Interview Insights

Mistral AI·Software Engineer·Onsite - Coding / Algorithms·Senior

SeniorPrefer not to say
Jun 2026

Summary

Mistral AI coding round with a multi-part GPU scheduling problem. The second part was a follow-up that felt almost too easy after the first.

Questions Asked (1)

Q1

You have a 2D array where each row is a day and each column is a cluster, with values representing remaining GPUs. A workload can freely switch clusters between days. What is the maximum total number of GPUs the workload can use across all days?

Algorithms & Data Structures
Author's notes

This was a follow-up to a harder part 1 and I almost overthought it.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Recognize that since the workload can switch clusters freely each day, the maximum GPUs usable per day is simply the maximum value in that day's row. Sum these daily maxima to get the total maximum GPUs across all days. This reduces to a straightforward O(rows × columns) scan of the 2D array.

Pro tip: Clarify any assumptions about constraints (e.g., non-negative values, no per-cluster capacity limits) before diving into the solution, and mention that the greedy per-day choice is optimal because days are independent.

1. Clarify the problem

Confirm that the workload can use any number of GPUs from a single cluster per day, and that switching clusters daily is free and unconstrained.

2. Identify the per-day maximum

For each day (row), find the maximum remaining GPUs across all clusters (columns). This is the most the workload can use that day.

3. Sum the daily maxima

Add the maximum values from each day to get the total maximum GPUs the workload can use across all days.

4. Analyze complexity

State that the algorithm runs in O(R × C) time and O(1) extra space, where R is the number of days and C is the number of clusters.

5. Discuss edge cases

Mention handling of empty rows, negative values (if possible), or single-day/single-cluster scenarios to ensure robustness.

Key Points to Mention

  • The problem reduces to summing the maximum value of each row because days are independent.
  • Greedy choice per day is optimal: no trade-offs exist across days.
  • Time complexity is O(R × C) and space complexity is O(1).
  • The workload can switch clusters freely, so no cumulative constraints apply.
  • Edge cases: empty input, rows with all zeros, or negative values (if allowed).
  • This is a classic example of a problem that seems complex but simplifies with the right observation.

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