Needed a second example from the interviewer before the problem clicked, which ate up like 15 minutes.
First, clarify the problem: what 'unique results' means, the input format, and constraints. Then, design an algorithm that uses a set or hash-based structure to deduplicate results, ensuring each result is generated only once. Finally, analyze the time complexity in terms of input size, considering the cost of deduplication and result generation.
Pro tip: Discuss trade-offs between different deduplication strategies (e.g., sorting vs. hashing) and mention how the choice affects time and space complexity. Also, consider if the problem allows for in-place deduplication or requires additional data structures.
Ask questions to understand what 'unique results' means, the input format, and any constraints (e.g., sorted input, memory limits). Confirm whether the algorithm should return a set of unique results or modify the input in place.
Decide between using a hash set for O(1) average-time insertions or sorting followed by linear scan for O(n log n) time. Consider the nature of the results (e.g., are they comparable?) and memory constraints.
Outline the steps: generate results, insert into a set to filter duplicates, and then return the set. If generating results is expensive, consider pruning duplicates during generation to avoid unnecessary work.
Break down the complexity into generation and deduplication. For example, if generating all results takes O(f(n)) and deduplication takes O(g(n)), the total is O(f(n) + g(n)). Discuss best, average, and worst cases.
Mention potential optimizations like early termination, using a Bloom filter for approximate deduplication, or leveraging sorted order. Address edge cases such as empty input, all duplicates, or large datasets.
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Coded two full solutions and talked through five alternatives, plus partially sketched a third in pseudo-code.
First, briefly restate the previous problem and its original solution to set context. Then, present an alternative approach with clear reasoning, write pseudo-code for a third distinct approach, and finally analyze the amortized complexity using aggregate or accounting methods, comparing trade-offs.
Pro tip: When discussing amortized complexity, explicitly state the potential function or credit invariant you're using—this shows rigor and often impresses interviewers at Google. Also, connect the alternative approaches to real-world trade-offs like memory vs. time.
Briefly summarize the previous problem and its original approach to ensure alignment and set the stage for alternatives.
Describe a different algorithm or data structure that solves the same problem, explaining why it works and its key steps.
Outline a third distinct method in pseudo-code, focusing on clarity and correctness, and mention any assumptions.
Use aggregate, accounting, or potential method to derive amortized time/space complexity, and compare with worst-case.
Summarize the pros and cons of each approach in terms of time, space, and implementation complexity, and state which is preferable in which scenario.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem scope, functional and non-functional requirements, and constraints with the interviewer. Then, outline a high-level architecture, dive into core components, and discuss trade-offs, scalability, and potential bottlenecks. Iterate based on feedback and always relate design choices back to requirements.
Pro tip: Drive the conversation by explicitly stating your assumptions and asking for validation—this shows collaboration and prevents you from solving the wrong problem. Also, quantify where possible (e.g., QPS, storage) to demonstrate practical scalability thinking.
Ask questions to understand the product's core features, expected scale (users, QPS, data volume), latency/consistency needs, and any constraints. Define what is in and out of scope.
Sketch the main components (e.g., clients, load balancers, services, databases, caches) and how they interact. Focus on data flow and meeting the core requirements.
Select 1-2 critical components (e.g., data storage, scaling strategy) and detail their design, including technology choices, data models, and algorithms. Explain how they handle scale and failures.
Discuss how the system scales (horizontal vs. vertical), handles bottlenecks (e.g., sharding, caching, async processing), and the trade-offs (e.g., consistency vs. availability, cost vs. performance).
Recap the design, highlight how it meets requirements, and mention potential improvements or next steps. Be open to interviewer feedback and adjust accordingly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First round had a behavioral component alongside the coding.
Use the STAR method to structure your answers, focusing on situations where you navigated ambiguity and adapted to changing requirements. Emphasize your thought process, how you sought clarity, and the positive outcomes achieved. Tailor each story to highlight Google's values like innovation, collaboration, and user focus.
Pro tip: Show self-awareness by acknowledging what you learned from the ambiguous situation and how you would approach it differently next time. This demonstrates growth and maturity, which Google values highly.
Briefly describe the project or situation, emphasizing the ambiguous or changing aspects. Keep it concise to leave time for your actions and results.
Detail the steps you took to bring clarity, such as asking questions, researching, or proposing a plan. Highlight how you prioritized tasks and communicated with stakeholders.
Describe how you adjusted your approach when new information emerged or priorities shifted. Show flexibility and a willingness to pivot.
Quantify the results if possible, and explain the impact on the team or project. Mention any recognition or lessons learned.
Relate the experience to Google's culture, such as embracing ambiguity, innovating, or collaborating effectively. Show enthusiasm for similar challenges.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.