Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Microsoft SWE interview with a data aggregation problem that looked straightforward but had enough edge cases to keep you busy for a while.

Questions Asked (1)

Given a dataset of concert location voting records (each with a voter ID, a location string, and an optional numeric weight defaulting to 1), write SQL and/or Python to find the most popular location by total vote weight. Cover how you handle ties, missing or malformed location text, and walk through a small example to verify. Also analyze time and space complexity.

Algorithms & Data StructuresData ModelingTechnical Trade-offs

Author's notes

I jumped straight into the SQL groupby-sum approach and felt pretty good about it, then they asked about ties and I kind of stumbled.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then present both SQL and Python solutions. For SQL, use aggregation with COALESCE and NULLIF to handle missing/malformed locations and weights, and use a window function or subquery to handle ties. For Python, use a dictionary to accumulate weights, filter invalid entries, and find max with tie handling. Walk through a small example and analyze complexity.

Pro tip: Demonstrate production awareness by discussing data quality (e.g., trimming whitespace, case normalization) and scalability (e.g., using SQL for large datasets, streaming in Python). Mention that ties should be explicitly defined (e.g., return all tied locations) and that weight defaults to 1 only when NULL, not when zero or negative.

1. Clarify requirements and edge cases

Ask about tie-breaking rules, definition of 'malformed' location (empty, whitespace, non-string), weight validation (negative, zero, non-numeric), and expected output format (single location or list).

2. Design SQL solution

Write a query that filters invalid locations, coalesces weight to 1 when NULL, groups by location, sums weights, and uses a window function (e.g., RANK) or subquery to return the top location(s) including ties.

3. Design Python solution

Implement a function that iterates through records, skips invalid locations, treats missing weight as 1, accumulates weights in a dictionary, and then finds the maximum weight and all locations achieving it.

4. Walk through a small example

Create a sample dataset with 5-6 records including a tie, a missing weight, and a malformed location. Manually compute the result and verify both SQL and Python outputs match.

5. Analyze time and space complexity

For SQL, note that grouping and sorting/window functions are O(n log n) due to sorting; for Python, dictionary accumulation is O(n) time and O(k) space where k is number of distinct locations.

Key Points to Mention

Handling missing weights: use COALESCE(weight, 1) in SQL and default to 1 in Python only when weight is NULL/None.
Malformed location handling: filter out NULL, empty strings, or whitespace-only strings; consider trimming and case normalization.
Tie handling: return all locations with the maximum total weight, not an arbitrary one; use RANK() or DENSE_RANK() in SQL and collect all max keys in Python.
Data types: ensure weight is numeric; handle non-numeric gracefully (e.g., cast or skip).
Complexity: SQL aggregation with sorting is O(n log n) time, O(n) space; Python dictionary approach is O(n) time, O(k) space.
Scalability: SQL is better for large datasets due to optimized aggregation; Python may need streaming or chunking for huge data.

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