Mistral AI·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Interviewed for a software engineer role at Mistral AI and got a simulation/array manipulation problem that looked deceptively simple at first glance. The question had some interesting edge cases once I actually thought it through.

Questions Asked (1)

You're given an array of GPU cluster capacities and a list of daily usage events (each event has a day, a cluster index, and a GPU count consumed). Write a function that returns a 2D array where each row shows the remaining GPU capacity per cluster at the end of that day. Clusters reset to full capacity each new day.

Algorithms & Data Structures

Author's notes

My first instinct was to just iterate through the events and subtract, which is mostly right, but I fumbled the reset logic initially.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then design an efficient algorithm that groups events by day and tracks remaining capacity per cluster. Use a hash map or array to store current capacities, resetting them at the start of each day, and produce a 2D array of remaining capacities for each day that has events.

Pro tip: Mention that you would sort events by day or use a dictionary keyed by day to avoid scanning all days, and explicitly handle days with no events by not including them in the output unless specified otherwise.

1. Clarify requirements and edge cases

Ask about input format, whether days are consecutive, if multiple events can occur on the same day, and if clusters can be over-consumed. Confirm output should only include days with events or all days up to the max day.

2. Choose data structures

Use a dictionary mapping day to a list of events, or sort events by day. Maintain an array of remaining capacities initialized to the given capacities, resetting it at the start of each day.

3. Process events day by day

Iterate through days in sorted order. For each day, reset capacities to full, then apply all events for that day by subtracting the consumed GPUs from the corresponding cluster.

4. Record remaining capacities

After processing all events for a day, append a copy of the current capacities array to the result. Ensure you copy the array to avoid mutation issues.

5. Analyze complexity and test

State time complexity O(E + D*C) where E is number of events, D is number of distinct days, C is number of clusters, and space complexity O(D*C) for output. Walk through a small example to verify correctness.

Key Points to Mention

Handling multiple events on the same day and ensuring order doesn't matter for subtraction.
Resetting capacities at the start of each day, not after each event.
Using a dictionary or sorting to group events by day efficiently.
Copying the capacities array when adding to the result to avoid aliasing.
Considering days with no events: whether to include them in output or skip.
Time and space complexity analysis, and potential optimizations like using a difference array if events are sparse.

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