Mid-level interview questionAsked at Scale AI · interview write-up
Design an API that serves text embeddings and classification, for both interactive and batch requests.
Model answer
Two endpoints, one encoder: classification is a light head on the embedding. Interactive calls are synchronous, with dynamic batching to a latency target.
Batch is an async job: submit a file, get a job id, poll or take a webhook, read results from storage, trading latency for cost. Every response names the model version, because vectors from different versions aren't comparable.
Practise the rest
17 Embeddings questions, 492 in all, each with a model answer. Free.
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- Your team switches to a newer embedding model with the same 384 dimensions and starts embedding incoming queries with it. Search quality collapses, but nothing errors. Explain what happened and how you'd run the migration.
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