Microsoft·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Microsoft SWE interview with a system design coding problem that looked deceptively straightforward. The core challenge was about weighted sampling across multiple data sources when the weights don't divide cleanly into a fixed batch size, which took me a minute to fully appreciate.

Questions Asked (1)

Design and implement a batch dataloader that draws samples from multiple data sources according to given weights. Each batch must be a fixed size, but the weights may not divide evenly into that size. How do you ensure the long-run proportions across batches stay close to the target weights?

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The easy case (weights like 0.3/0.3/0.4 with batch size 10) clicks immediately, three items, three items, four items, done.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a weighted sampling algorithm that maintains long-run proportions using a deterministic or probabilistic approach. Discuss implementation details, trade-offs, and how to handle edge cases like non-integer weights and batch size constraints.

Pro tip: Mention using a deterministic method like the largest remainder method or a probabilistic method like weighted random sampling with a correction mechanism, and highlight the importance of reproducibility and testing for statistical properties.

1. Clarify Requirements

Ask about data sources, weights, batch size, and whether exact proportions are required per batch or just in the long run. Confirm if randomness is acceptable or if determinism is needed.

2. Choose Sampling Strategy

Decide between deterministic approaches (e.g., largest remainder method) and probabilistic approaches (e.g., weighted random sampling with correction). Consider using a running count to adjust future batches.

3. Design Algorithm

Outline the algorithm: compute expected counts per source per batch, allocate integer counts, and handle remainders. For probabilistic, use a weighted random selection but adjust probabilities based on past deviations.

4. Implement and Handle Edge Cases

Implement the algorithm, ensuring it handles cases where weights don't divide evenly, batch size is small, or sources are exhausted. Discuss how to maintain state across batches.

5. Analyze Trade-offs and Test

Compare deterministic vs. probabilistic methods in terms of fairness, complexity, and performance. Suggest testing with statistical measures like chi-square to verify long-run proportions.

Key Points to Mention

Weighted random sampling with correction (e.g., using a deficit counter)
Largest remainder method for deterministic allocation
Maintaining state across batches to track deviations
Handling non-integer expected counts and rounding
Trade-offs: determinism vs. randomness, complexity, and fairness
Testing for statistical convergence to target weights

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