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Capital One·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2025Remote

Summary

Capital One Data Scientist interview with a dense multi-part SQL question. One problem, but it covered basically everything: CTEs, NULLs, aggregates, UNION ALL, conditional logic. More of an exam than a conversation.

Questions Asked (1)

Q1

Given a student/exam/scores schema, write a single SQL statement (CTEs allowed) that: computes subject-level metrics for May 2025 including a floored average, a high-scorer count, and a low-score percentage (handling NULLs and subjects with zero attempts); labels each student-subject pair with a pass or remedial flag based on their best May score; and combines both result sets with UNION ALL, adding a level column and NULLs for inapplicable columns.

Data ModelingProduct Analytics & MetricsTechnical Trade-offs
Author's notes

This one wrecked me a little.

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AI HintsAI Generated

Suggested Approach

Break the problem into two CTEs: one for subject-level aggregates and one for student-subject pass/remedial flags. Then combine them with UNION ALL, ensuring column alignment and adding a level column to distinguish the rows. Handle NULLs and zero attempts explicitly using COALESCE and conditional aggregation.

Pro tip: Before writing the final query, verbally outline the CTE structure and column alignment to show your thought process. Mention that you'd test edge cases like subjects with no attempts and students with NULL scores to ensure robustness.

1. Understand the schema and requirements

Identify the tables (student, exam, scores) and their relationships. Clarify what 'floored average', 'high-scorer count', and 'low-score percentage' mean, and define pass/remedial thresholds.

2. Design the subject-level metrics CTE

Use conditional aggregation to compute metrics for May 2025. Handle NULLs with COALESCE and ensure subjects with zero attempts are included (e.g., via LEFT JOIN or UNION).

3. Design the student-subject flag CTE

For each student-subject pair, find the best May score and label as 'pass' or 'remedial' based on a threshold. Include only pairs with at least one attempt.

4. Combine with UNION ALL and align columns

Add a 'level' column to distinguish subject-level vs student-level rows. Use NULLs for columns not applicable to each level, ensuring both SELECT statements have the same number and order of columns.

5. Review for edge cases and performance

Check handling of NULLs, zero attempts, and date boundaries. Consider indexing and whether the query can be optimized for large datasets.

Key Points to Mention

  • Use of CTEs for readability and modularity
  • Conditional aggregation (CASE WHEN) for metrics like high-scorer count and low-score percentage
  • Handling NULLs with COALESCE and ensuring subjects with zero attempts are included
  • Defining pass/remedial based on best score (e.g., MAX) and threshold
  • UNION ALL with column alignment and a level column to differentiate row types
  • Edge cases: subjects with no attempts, students with NULL scores, date filtering for May 2025

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