Databricks·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Databricks data scientist interview with a technical problem that looked straightforward on the surface but had enough edge cases to keep you busy. The core task was a nearest-neighbor style matching problem in SQL or Python, and the real challenge was handling NULLs and deciding whether to standardize features before computing distances.

Questions Asked (1)

Given two tables of numeric feature vectors, find the top 5 most similar candidate rows for each target row using MSE as the distance metric. Return the target ID, candidate ID, distance, and rank ordered by target then rank.

Algorithms & Data StructuresTechnical Trade-offsData Modeling

Author's notes

The MSE formula itself wasn't the hard part.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (table sizes, dimensionality, whether approximate results are acceptable) and then propose a scalable solution using distributed computing (e.g., Spark) to compute pairwise MSE distances, followed by a window function to rank candidates per target. Emphasize efficiency by discussing partitioning, broadcasting, and avoiding full cross joins when possible.

Pro tip: Mention that in practice, you'd use approximate nearest neighbor libraries (like FAISS or Annoy) or Spark's LSH to reduce the O(n*m) complexity, but be ready to explain the exact solution for small data. Also, highlight the importance of handling ties in distance ranking.

1. Clarify requirements and constraints

Ask about data size, dimensionality, whether exact or approximate results are needed, and if there are any performance SLAs. This determines whether a brute-force approach or an approximate method is appropriate.

2. Design the distance computation

For each target-candidate pair, compute MSE = average of squared differences across features. In a distributed setting, consider broadcasting smaller table or using bucketing to reduce shuffle.

3. Rank and select top 5 per target

Use a window function (e.g., ROW_NUMBER() OVER (PARTITION BY target_id ORDER BY distance ASC)) to rank candidates for each target, then filter to rank <= 5.

4. Optimize for scale

Discuss trade-offs: full cross join is O(n*m) and may be infeasible; alternatives include approximate nearest neighbors, dimensionality reduction, or clustering to prune candidates.

5. Handle edge cases and output

Address ties (use RANK or DENSE_RANK if needed), missing values, and ensure final output is ordered by target_id then rank. Consider partitioning output for efficient storage.

Key Points to Mention

MSE formula: (1/d) * sum((target_i - candidate_i)^2)
Use of window functions for ranking (ROW_NUMBER, RANK, DENSE_RANK)
Scalability: cross join vs. approximate nearest neighbors (e.g., LSH, FAISS)
Partitioning and broadcasting strategies in Spark to optimize performance
Handling ties in distance: implications of ROW_NUMBER vs. RANK
Output ordering and format: target_id, candidate_id, distance, rank

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.