nvidia

NVIDIA Engineering Data Analyst Case Interview: GPU Telemetry, Experimentation, and Business Impact

This NVIDIA case simulates how a Data Analyst partners with engineers and product leaders to diagnose GPU performance/reliability issues and quantify their business impact. You’ll work from a concise prompt and lightweight schemas (e.g., cloud GPU utilization logs, game/driver telemetry, and a releases table) to: 1) clarify the problem and success metrics (e.g., p95 frame-time, crash rate, datacenter GPU utilization, attach-rate or ASP implications); 2) propose the right KPIs and guardrails, noting GPU-specific nuances like SKU/architecture, driver/CUDA versions, thermals/throttling, VRAM OOMs, region/time-of-day, and workload mix; 3) write SQL to join/clean data (dedupe device_ids, handle nulls, window functions for rolling p95/p99, cohort by driver release, compute incident rates with correct denominators); 4) explore and explain anomalies with first-principles reasoning (distribution skew, seasonality, Simpson’s paradox, hardware segmentation) and produce quick visual summaries you would share with an engineer/PM; 5) design an experiment or quasi-experiment for a driver fix or scheduling change (treatment/control definition, power/sample size, primary/secondary metrics, ramp/rollback, bias and interference risks); 6) estimate business impact with a back-of-the-envelope model (e.g., minutes of instability avoided across MAUs, potential RMA reduction, cloud cost savings, revenue or customer-experience deltas) and articulate trade-offs; 7) deliver a concise exec-level recommendation and an engineering-ready follow-up plan. Interviewers typically probe deeply on assumptions and ask “why” repeatedly; expect live SQL/pseudocode, statistical reasoning under time pressure, and clear, candid communication consistent with NVIDIA’s culture of intellectual rigor and collaboration. Bonus points for demonstrating familiarity with large-scale telemetry patterns, GPU/workload context, and pragmatic use of Python/R for checks (e.g., quick EDA, confidence intervals) while keeping the focus on impact and decision-making.

engineering

8 minutes

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About This Interview

Interview Type

PRODUCT SENSE

Difficulty Level

4/5

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