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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Google coding screen, pretty short. One probabilistic simulation question and that was basically it.

Questions Asked (1)

Q1

Write a function that simulates flipping a fair coin 25 times and returns the total number of heads.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Seems trivial but I second-guessed myself on whether to use a library random function or implement something from scratch.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then implement a simple simulation using a random number generator. After presenting the basic solution, discuss potential improvements and trade-offs, such as using a binomial distribution for efficiency or ensuring randomness quality.

Pro tip: Demonstrate awareness of randomness pitfalls: mention that using a language's built-in random function with a proper seed is crucial for fairness, and that for large numbers of flips, a binomial distribution can be more efficient.

1. Clarify requirements

Ask if the function should be deterministic (e.g., for testing) or truly random, and confirm the output format (integer count).

2. Design the simulation

Decide on using a loop with a random number generator, where each iteration simulates one flip and increments a counter for heads.

3. Implement the function

Write clean code with meaningful variable names, ensuring the random number generation is unbiased (e.g., using rand() < 0.5 for heads).

4. Test and validate

Run the function multiple times to check that results vary and are within expected range (0-25), and consider edge cases like 0 flips.

5. Discuss trade-offs and optimizations

Mention alternative approaches like using a binomial distribution for efficiency, and discuss the importance of randomness quality.

Key Points to Mention

  • Random number generation: using a uniform distribution and avoiding modulo bias.
  • Time and space complexity: O(n) time, O(1) space for the simulation.
  • Alternative approach: using binomial distribution for large n.
  • Testing: how to test a random function (e.g., statistical tests, mocking randomness).
  • Edge cases: handling n=0 or negative inputs.
  • Language-specific details: e.g., Python's random.random() or C++'s uniform_int_distribution.

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