Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Google SWE coding round focused on a sampling problem. The interviewer had you pseudocode first before writing actual code, which was a reasonable constraint but still caught me a bit off guard mid-interview.

Questions Asked (1)

Write a sampling program. The interviewer may ask about reservoir sampling, weighted sampling, or rejection sampling depending on the use case. Start by clarifying which variant is needed, then describe your approach in pseudocode before moving to a full implementation.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The pseudocode-first constraint sounds easy but it actually slowed me down because I kept wanting to just write code.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the sampling requirements: is the data streamed or static, is the sample size fixed, and are weights involved? Then outline the appropriate algorithm (reservoir, weighted, or rejection) in pseudocode, discussing time/space complexity and trade-offs. Finally, provide a clean implementation in a language of your choice, handling edge cases and explaining your design choices.

Pro tip: Explicitly state the assumptions you're making (e.g., uniform random number generator available, stream length unknown) and how they affect your choice. This shows you think about real-world constraints and not just the algorithm.

1. Clarify Requirements

Ask questions to determine the sampling variant: Is the data a stream or a static array? Is the sample size fixed? Are there weights? What are memory constraints?

2. Choose Algorithm

Based on requirements, select reservoir sampling (uniform from stream), weighted sampling (e.g., A-Res, A-ExpJ), or rejection sampling (for specific distributions). Justify your choice.

3. Describe Pseudocode

Write clear pseudocode for the chosen algorithm, explaining each step and the role of randomness. Mention time and space complexity.

4. Implement and Test

Provide a full implementation in a suitable language, handling edge cases (e.g., empty stream, sample size larger than stream). Briefly discuss testing strategy.

5. Discuss Trade-offs

Compare alternatives: e.g., reservoir vs. rejection for uniform sampling, or weighted reservoir vs. precomputing cumulative weights. Mention scalability and precision issues.

Key Points to Mention

Reservoir sampling: maintains a uniform sample of size k from a stream of unknown length in O(n) time and O(k) space.
Weighted sampling: algorithms like A-Res or A-ExpJ for weighted reservoir sampling, or using cumulative weights for static data.
Rejection sampling: useful for sampling from complex distributions by using a simpler proposal distribution; efficiency depends on acceptance rate.
Random number generation: ensure uniform random integers/floats are used correctly; avoid modulo bias.
Edge cases: empty input, k=0, k > n, and handling of duplicates or ties in weights.
Complexity analysis: time and space for each approach, and how they scale with stream size and sample size.

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