diagnosis

The OpenAI FDE deep-dive where you're expected to walk the full stack (network, queueing, tokenization, prefill, KV cache, batching, decode) in order, with a measurement at each hop. The triage sequence, with the numbers that tell you which layer is guilty.

The Databricks signature triage question. Strong candidates don't list optimizations, they run a diagnostic decision tree: plan first, then scan, shuffle, join strategy, files. Here's the tree.

The regression with no smoking gun, no deploy to blame, no single bad night. Staff-level triage is about reading the trend, not the last run, and the four slow-burn causes that a one-night comparison can't see.

The most common escalation in any data deployment, and the one that quietly ends trust in the platform. Four causes produce it, one of them means nobody is wrong, and you cannot tell them apart without running the ladder in order.

The deployment worked. The software runs, the data flows, the demo landed. Nobody is using it. Two of the three research sets we vetted surfaced this from different angles, and it is the failure mode that ends renewals.

The most common real ticket in applied AI, and it contains almost no information. What you do in the first hour decides whether you spend a day on this or three weeks.