Microsoft·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Microsoft SWE coding round, one question but it had enough layers to keep me busy. Basically a mini SQL engine in Python, which sounds simple but the edge cases add up fast.

Questions Asked (1)

Given a list of row dictionaries where each maps column names to values, implement a query function that accepts a list of column names to project and a list of filter tuples (column, operator, value) with operators <, >, and =. Return only the rows matching all filters, projected to the specified columns.

Algorithms & Data StructuresAPI & IntegrationsTechnical Trade-offs

Author's notes

I got the basic structure down pretty quick: filter first, then project.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then outline a simple, efficient algorithm that iterates over rows, applies all filters, and projects the selected columns. Discuss trade-offs between readability and performance, and mention potential optimizations like indexing or vectorization.

Pro tip: Emphasize that you would validate inputs and handle missing columns gracefully, and suggest that for large datasets, pushing filters down or using columnar storage can drastically improve performance.

1. Clarify requirements and edge cases

Ask about data types, missing columns, empty filters, and performance expectations. Confirm whether filters are combined with AND and if projection order matters.

2. Design the algorithm

Outline a straightforward approach: iterate through each row, check all filter conditions, and if all pass, build a new dictionary with only the projected columns. Discuss time complexity O(n*m) where n is rows and m is filters.

3. Implement with clean code

Write a function that takes rows, columns, and filters. Use a helper to evaluate each filter, and handle operators via a dispatch dictionary or if-elif chain. Ensure missing columns raise appropriate errors.

4. Test and validate

Walk through test cases: no filters, multiple filters, empty result, missing columns, and different data types. Verify that projection returns only requested columns in the correct order.

5. Discuss optimizations and trade-offs

Mention that for large datasets, you could build indexes on filter columns, use vectorized operations (e.g., pandas), or push filters down to a database. Balance simplicity with performance based on context.

Key Points to Mention

Time and space complexity analysis (O(n*m) time, O(k) space per row for projection)
Handling of missing columns or type mismatches gracefully (e.g., raise ValueError or skip)
Use of a dispatch table for operators to avoid repetitive if-elif chains
Immutability: returning new dictionaries rather than modifying input rows
Potential for short-circuit evaluation when a filter fails
Scalability considerations: indexing, vectorization, or database pushdown for large datasets

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