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Capital One·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

SQL question for a Data Scientist role at Capital One, travel-themed scenario with a pretty standard aggregation problem.

Questions Asked (1)

Q1

Given a table of daily weather records by country, write a SQL query to find the country with the most days of sunny weather.

Data ModelingProduct Analytics & Metrics
Author's notes

Pretty approachable once you see it's just a conditional aggregate and a sort.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the table schema and the definition of 'sunny weather' (e.g., a specific weather condition value). Then write a query that groups by country, counts the number of sunny days, and returns the country with the highest count, handling ties appropriately.

Pro tip: Mention that you would validate the definition of 'sunny' with stakeholders and consider using a window function or a subquery to handle ties, showing awareness of data quality and business context.

1. Clarify the schema and definitions

Ask about the table structure (columns like date, country, weather_condition) and confirm what constitutes 'sunny' (e.g., weather_condition = 'Sunny').

2. Filter for sunny days

Use a WHERE clause to select only rows where the weather condition indicates sunny weather.

3. Aggregate by country

Group the filtered data by country and count the number of sunny days per country using COUNT(*).

4. Identify the top country

Order the results by the count in descending order and limit to the top row, or use a window function to handle ties if needed.

5. Consider edge cases and performance

Discuss handling ties (e.g., using RANK() or DENSE_RANK()), and mention indexing or partitioning for large datasets.

Key Points to Mention

  • Assumptions about the table schema and the definition of 'sunny'
  • Use of GROUP BY and COUNT to aggregate sunny days per country
  • Handling ties: either return all countries with the max count or pick one arbitrarily
  • Using ORDER BY with LIMIT or window functions like RANK() for top-N selection
  • Data quality considerations: missing values, inconsistent weather condition labels
  • Performance implications for large datasets (indexes, partitioning)

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