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Databricks·Data Scientist·Technical Phone Screen·Senior

Senior
May 2026

Summary

Databricks data scientist interview with a meaty coding/SQL problem around nearest-neighbor matching across two datasets. The question had a lot of moving parts and the discussion went pretty deep into metric tradeoffs.

Questions Asked (2)

Q1

Given a source table and a target table that share the same numeric feature columns, write Python or SQL to find the top 5 most similar rows in target for each row in source. You need to define your similarity metric, explain how you handle NULLs, and output source_id, target_id, distance, and rank.

Algorithms & Data StructuresData ModelingTechnical Trade-offs
Author's notes

This took me longer to set up than I expected, mostly because of the NULL handling piece.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the similarity metric (e.g., Euclidean or cosine) and how to handle NULLs (e.g., imputation or exclusion). Then outline a scalable solution using a cross join with distance calculation and window functions to rank and filter top 5 per source row, leveraging Databricks capabilities like broadcast joins or approximate nearest neighbor search.

Pro tip: Mention that for large datasets, exact cross join is infeasible; propose using Databricks' Approximate Nearest Neighbor (ANN) or locality-sensitive hashing (LSH) to scale, and discuss trade-offs between accuracy and performance.

1. Clarify requirements and assumptions

Ask about data size, whether exact or approximate similarity is needed, and if NULLs should be treated as zeros, imputed, or rows excluded. Confirm the output format and ranking method (e.g., ties).

2. Define similarity metric and NULL handling

Choose a metric like Euclidean distance or cosine similarity based on feature types and scale. Explain NULL handling: impute with mean/median, treat as separate category, or exclude rows with NULLs, ensuring consistency across source and target.

3. Implement distance calculation and ranking

Use a cross join to compute pairwise distances, then apply a window function (e.g., ROW_NUMBER() OVER (PARTITION BY source_id ORDER BY distance)) to rank target rows per source. Filter to top 5.

4. Optimize for scale and Databricks

For large data, use broadcast join if one table is small, or leverage Databricks' ANN with MLlib or Delta Lake's built-in indexing. Discuss partitioning and caching strategies.

5. Validate and output results

Check for ties, ensure correct ranking, and output source_id, target_id, distance, and rank. Optionally, add a sanity check on a small sample.

Key Points to Mention

  • Choice of similarity metric (Euclidean, cosine, Manhattan) and its implications for feature scaling and data distribution.
  • NULL handling strategies: imputation, exclusion, or treating NULL as a distinct value, and their impact on distance calculations.
  • Scalability considerations: cross join complexity O(n*m), use of approximate methods like LSH or ANN for large datasets.
  • Databricks-specific optimizations: broadcast joins, Delta Lake, MLlib's ANN, and Photon engine for performance.
  • Ranking with window functions and handling ties (e.g., using ROW_NUMBER, RANK, or DENSE_RANK).
  • Output format and potential need for additional columns like similarity score or feature contributions.

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

Q2

What are the tradeoffs between using MSE versus RMSE as your distance metric here, and would you consider any alternative metrics like cosine distance or Manhattan distance?

Technical Trade-offsProduct Analytics & Metrics
Author's notes

MSE vs RMSE is basically just a monotonic transformation so the ranking doesn't change, which I said correctly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the context—what model, data, and business objective—since the choice of metric depends on these. Then compare MSE and RMSE in terms of interpretability, sensitivity to outliers, and optimization properties, and finally discuss when alternatives like cosine or Manhattan distance might be more appropriate.

Pro tip: Mention that RMSE is often preferred for reporting because it's in the same units as the target, while MSE is better for optimization due to its smooth derivative. Also, note that at Databricks, scalability and distributed computation might influence metric choice, so consider computational efficiency.

1. Clarify the context

Ask or state assumptions about the problem: is it regression, clustering, or recommendation? What is the scale of data, and what business metric matters?

2. Compare MSE and RMSE

Discuss that MSE penalizes larger errors more heavily and is differentiable everywhere, while RMSE is interpretable in original units but still sensitive to outliers.

3. Evaluate alternatives

Consider cosine distance for high-dimensional or sparse data where direction matters more than magnitude, and Manhattan distance for robustness to outliers or when features have different scales.

4. Align with business goals

Tie the choice back to the product or business objective: e.g., if large errors are costly, MSE/RMSE; if only ranking matters, cosine.

5. Conclude with a recommendation

Summarize which metric you would choose and why, acknowledging that it depends on the specific use case and data characteristics.

Key Points to Mention

  • MSE is more sensitive to outliers than RMSE because errors are squared before averaging, but RMSE is in the same units as the target.
  • RMSE is often used for reporting and communication, while MSE is preferred for optimization due to its convexity and smooth gradient.
  • Cosine distance ignores magnitude and focuses on orientation, making it useful for text or high-dimensional data where scaling is irrelevant.
  • Manhattan distance (L1) is more robust to outliers and can be better when data has many outliers or when features are on different scales.
  • The choice of metric should align with the evaluation criteria and business impact—e.g., if large errors are unacceptable, MSE/RMSE; if only relative ordering matters, cosine.
  • At Databricks, consider distributed computing implications: some metrics are easier to parallelize than others.

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