The twist is that you're not just coding, you're reviewing every diff the AI produces before accepting it.
Start by clarifying requirements and constraints, then outline a high-level design covering API endpoints, data model, and shortening algorithm. Use the AI assistant to generate code for each component, but critically review and adjust for scalability and correctness. Finally, test the implementation and commit with a clear message.
Pro tip: Emphasize trade-offs in your design choices, such as using a counter-based approach vs. hash-based for short codes, and discuss how you'd handle collisions and scalability. This shows you think beyond just getting it working.
Ask questions to understand expected scale, latency, persistence requirements, and any specific features like custom aliases or analytics. This ensures the design meets the actual needs.
Outline the API endpoints (e.g., POST /shorten, GET /{code}), the data model (mapping short code to long URL), and the shortening algorithm (e.g., base62 encoding of a unique ID). Consider using a database for persistence.
Use the AI to generate code for each component, but review and refine the output. Ensure the code handles edge cases like duplicate URLs, invalid input, and collisions.
Write unit and integration tests to verify functionality, including creating short URLs, redirecting, and handling errors. Test scalability aspects if possible.
Commit the code with a descriptive message, and include a README explaining the design decisions and how to run the service. This demonstrates professionalism and clarity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Base62 was the obvious answer and I said it immediately, which was fine.
Start by framing the problem and the constraints that drove your design choices, then walk through each decision (collision handling, encoding, key length) with clear trade-offs and why you chose that path. Emphasize how your choices balanced performance, memory, and scalability in a production ML context.
Pro tip: Quantify the impact of your decisions with concrete metrics (e.g., 'reduced collisions by 30%' or 'cut memory usage by 40%') and acknowledge any limitations or alternative approaches you considered. This shows you think like an engineer, not just a coder.
Briefly describe the problem you were solving (e.g., feature hashing for a recommendation model) and the key constraints (latency, memory, throughput) that shaped your design.
Describe the collision resolution strategy you chose (e.g., open addressing, chaining, or multiple hash functions) and why it was appropriate given the constraints. Mention how you measured and mitigated collision impact.
Explain the encoding scheme (e.g., one-hot, binary, or learned embeddings) and why it fit the data and model. Discuss trade-offs like sparsity, dimensionality, and interpretability.
Detail how you selected the key length (e.g., 32-bit vs 64-bit) by balancing collision probability, memory footprint, and computational efficiency. Reference the birthday paradox or empirical tests if applicable.
Conclude with the results (e.g., improved model accuracy, reduced memory) and what you would do differently next time, showing iterative thinking.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with an in-memory store first to get something working, then flagged that a real deployment would need something durable.
Start by clarifying the service's data requirements and access patterns, then explain how you evaluated persistence options against those needs. Focus on the trade-offs you weighed (e.g., latency, scalability, consistency) and justify your final choice with concrete examples or metrics.
Pro tip: Tie your decision to business impact—such as how it reduced latency for model inference or improved feature freshness—and acknowledge any limitations or future considerations to show foresight.
Outline the key requirements of the service, such as data volume, read/write patterns, latency needs, and consistency requirements.
Enumerate the persistence layer candidates you considered (e.g., relational databases, NoSQL stores, data lakes, feature stores) and their core characteristics.
Compare the options against your requirements, discussing trade-offs like scalability vs. consistency, cost, operational complexity, and integration with ML workflows.
Explain why you chose the final persistence layer, referencing specific criteria and any prototyping or benchmarking you did.
Summarize the results (e.g., performance improvements, ease of maintenance) and mention any lessons learned or future adjustments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.