Databricks interview questions

Run a classifier on both sides: on the prompt before the model, and on the response after, because a harmless prompt can still produce a harmful answer. Start from a taxonomy, such as violence, self-harm, hate and jailbreaks.

A small fast model scores every request; borderline ones go to a larger judge, samples to humans. Tune thresholds per category, since false positives block real users. Feed red-team findings back into training.

Index more than files: parsed symbols with signatures and docstrings, the import and call graph, tests, docs and recent diffs. Chunk on syntax boundaries, a whole function with its path and class, not fixed windows.

Rank with several signals: exact identifier search, embeddings for intent, and graph expansion from symbols in the open file or stack trace, then a reranker. Cut noise by skipping vendored, generated and build files. Measure recall of the files the real fix touched, then task success: do the tests pass?

Turn each asset into a searchable card: a table's name, columns and comments, tags, owner, sample values and lineage; a notebook's title, cell summary, and the tables it reads and writes.

Retrieve with exact match on table and column names plus embeddings on descriptions, filtered by access permissions first. Rerank with a cross-encoder and usage signals: query popularity, freshness, certified status, and lineage closeness to tables already in the session. Evaluate on logged requests: recall at k of the table the user really queried, then whether the SQL runs correctly.

Queries mix exact identifiers, like a table name or catalogue path, with intent, like churn by region. Run a keyword index over names, paths and column names, and embeddings over descriptions and comments.

Normalise identifiers first, splitting snake_case and camelCase so cust_id matches customer id. Merge them with reciprocal rank fusion, but let an exact name match go straight to the top. Filter by permissions first, and tune on search logs.