Databricks Data Scientist Interview Questions
The questions to prepare for a Databricks Data Scientist interview. Questions from real interview reports rank first. Updated weekly.
Compute daily active users and a 7-day rolling average using a CTE, distinct counts, and window functions.
DatabricksCount recurring high-confidence ticket themes in April using joins, filtering, and grouped aggregation.
DatabricksClean raw status text with TRIM and LOWER, filter unusable rows, and count usable events by cleaned status.
DatabricksDesign a shared feature store for training and low-latency inference across many ML systems with strict freshness and consistency needs.
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Explain how feature engineering improves supervised model performance and how to validate its impact with proper evaluation.
DatabricksExplain how you evaluate models using the right metrics, validation strategy, and error analysis for the problem.
DatabricksDiagnose a post-release KPI drop by separating instrumentation issues from real behavior changes and tracing the problem through the metric hierarchy.
DatabricksCompute the mean and variance of a numeric dataset from first principles.
DatabricksDesign an A/B test for a new platform feature, including success metrics, power, guardrails, and a clear ship decision.
DatabricksCompare batch and streaming data processing, including when each fits best in a pipeline.
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