NVIDIA Software Engineer Interview Questions

I would first clarify what near real time means, because that determines whether I need sub-second updates or just a few seconds of delay. At a high level, I would ingest telemetry into a stream processing layer, aggregate the data into time windows, and push updates to the dashboard through a fast read layer or websocket connection. The reason I choose this architecture is that the dashboard should remain responsive even if the raw telemetry volume is huge. I would also think about sampling, caching, and handling late-arriving data. The trade-off is that more freshness usually means more infrastructure cost, so I would tune the system to the business need rather than overbuilding it.

I would use two pointers, one at the beginning and one at the end. I would swap the characters, move the pointers inward, and continue until they meet. The reason I choose this approach is that it is simple, uses O(1) extra space, and runs in O(n) time. I would also mention that in some languages strings are immutable, so I would need to work on a mutable character array instead. Edge cases include empty strings and single-character strings.

Before designing it, I would clarify whether jobs are short-lived, long-running, or GPU-intensive, because that affects scheduling and retries. At a high level, I would use a durable queue for job submission, workers that pull jobs when they are ready, and a metadata store for job status and ownership. I would also design for idempotency so retries do not accidentally execute the same job twice in a harmful way. The reason I choose a queue-based design is that it smooths out bursts and lets the system scale independently on the producer and consumer sides. I would also discuss visibility timeouts, dead-letter queues, and monitoring for stuck jobs.

I would use two pointers, one for each array. I would move the pointer for the smaller value forward until the values match or one array ends. The reason I choose this approach is that it is O(n + m) time and O(1) space, and it works especially well when both arrays are already sorted. If duplicates matter, I would explain whether I need unique intersections or repeated matches. I would also mention that a hash set is easier if the arrays are not sorted.

I would first clarify what kinds of images are being cached, how often they are reused, and how important cache freshness is. At a high level, I would keep thumbnails or resized variants in a cache, use an eviction policy like LRU, and store metadata so I can quickly decide whether a cached image is still valid. The reason I choose image-specific caching is that images can be expensive to regenerate, but I also do not want the cache to consume too much memory. I would think about compression, cache key design, and how to prevent stale content from living forever. If the system has multiple clients, I would also discuss CDN support and invalidation.