Where it comes from
- At Databricks, a recruiter described this to one candidateSource 1Anyone been through interviews for AI FDE at Databricks ? (post by u/Haunting_Ad3263)PublisherReddit r/cscareerquestionsukSource typecandidate report on RedditSource 2Anyone been through interviews for AI FDE at Databricks ? (comment by u/Haunting_Ad3263)PublisherReddit r/cscareerquestionsukSource typecandidate report on Reddit More Databricks questions The Databricks interview guide
How to answer
One candidate for Databricks’ AI role reported, in August 2026, being told that the coding round focuses on applied data science and traditional machine learning. Source 1Anyone been through interviews for AI FDE at Databricks ? (post by u/Haunting_Ad3263)PublisherReddit r/cscareerquestionsukSource typecandidate report on RedditSource 2Anyone been through interviews for AI FDE at Databricks ? (comment by u/Haunting_Ad3263)PublisherReddit r/cscareerquestionsukSource typecandidate report on Reddit A churn baseline fits, and the hard parts sit outside the model.
- Start from the decision. Ask who acts on the score and how many accounts they can handle. That sets the metric. Then fix the label: what counts as churn, the prediction date and the horizon (“active on the first of the month, cancels before the next”).
- Build features as of the prediction date. Features use only events before it; the label uses only events after it. Say so out loud, because that is where leakage starts.
- Split by time. Train on earlier snapshots and test on a later one, and check that every training label was fully observed before the test date.
- Put baselines first. The base rate, then the rule the team would use without you, such as days since the last login. A logistic regression on a few features must beat the rule, not the base rate.
- Measure what the decision needs. When churn is rare, accuracy rewards predicting that no one leaves. If the team calls a fixed number of accounts, report precision and recall in that top slice, plus average precision (the precision-recall summary), and check calibration if the score will be read as a probability.
- Answer “good enough” for a named use. Ranking a call list is a lower bar than triggering discounts automatically. Name the proof: a holdout of flagged accounts nobody calls, because finding churners is not saving them.
The trap is a random split and a near-perfect score. Treat a suspiciously good result as a bug until you find the leak.
Follow-ups
What the interviewer may ask next, once your first answer is on the table.
- Your first model scores almost perfectly. What do you check before you tell anyone?
- The success team can only call so many accounts a month. How does that change what you measure?
- How would you know the calls prevent churn, rather than only finding the accounts that were leaving anyway?
- Why a logistic regression and not gradient boosting? What would make you switch?
Where answers go wrong
- Splitting rows at random, so the model learns from later months and is tested on earlier ones, and the same account sits on both sides.
- Computing features over the whole table, including events after the prediction date, then trusting a near-perfect score.
- Reporting accuracy when most accounts stay. A model that says nobody churns scores well and helps no one.
- Comparing the model with nothing, instead of with the one-line rule the team would use without it.
- Calling it good enough without saying good enough for which decision.
Answer this in two minutes
Write the answer you would say out loud. The clock starts with your first word.
Model answer
The table is events(account_id, ts, event), where event is signup, login, ticket or cancel. “Before any model: who uses the score?