Design the evaluation you run against a serving deployment, not against a model.

Test the complete serving bundle with contract fixtures, representative tasks and load-aware outcomes. Preserve paired results, calibrate the scorer and distinguish a relative improvement from meeting an absolute product requirement.

Updated Sep 2026 · Learn how AI infrastructure works through explanations, worked examples and diagrams, then practise applying it to interview questions.

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Work through the numbers

Change the assumptions in a free calculator, then explain what the result establishes and what still needs measurement.

The concepts behind this question

Use these concept explanations to review the ideas behind this question.

Foundational

🧮 Open Weights & Serving Engines

Serving Benchmarks That Do Not LieDesign reproducible serving experiments with realistic arrivals, cache state and failure accounting. Interpret latency-qualified throughput and conditional per-rank bounds without turning optimistic kernel estimates into production capacity.

Advanced

🚀 Inference & Serving🔒 Premium

Tokens, Embeddings and LogitsTrace token IDs, embedding rows and next-token scores. Work out which tensors grow with a batch, when all-position logits waste memory, and why token probabilities do not establish factual confidence.

Advanced

🚀 Inference & Serving🔒 Premium

Reasoning Budgets and Answer LatencyReasoning tokens consume generation time and retained state before an answer becomes usable. Separate the first generated token from the first answer token, reserve output capacity, and evaluate reasoning limits against task success.

Advanced

🧮 Open Weights & Serving Engines🔒 Premium

Pretraining, Fine-Tuning and DistillationUnderstand how a base model becomes a useful application model. Separate training objectives from LoRA and quantization, compare distillation with retrieval, and choose the smallest intervention supported by your evaluation.

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UP NEXT ON YOUR JOURNEY

Next in this trackEvery response must be valid JSON against a schema. How do you serve that at scale?Next in this trackOne tenant's traffic is pushing everyone else past their latency objective. What do you do?Next in this trackThree open-weights models could serve your product. How do you choose?

FEDITOR'S NOTE

Scored on complementary datasets and checks, reproducible manifests, paired slice evaluation, scorer limitations, uncertainty-aware acceptance and retained failures under load.

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