Debugging and Code Optimization Questions
Practical debugging skills and techniques for improving code performance and complexity. Topics include tracing and reproducing bugs, stepping through execution, reasoning about time and space complexity, refactoring for performance, and applying algorithmic optimizations. Candidates should be able to demonstrate logical debugging approaches and make safe, measurable performance improvements to working code.
HardSystem Design
153 practiced
Design a tracing and observability plan to debug a problem where a user request sometimes results in duplicate side-effectful operations (for example: double charges) across multiple microservices. Describe required instrumentation, trace correlation, sampling rules, business checks (idempotency keys), and how you'd use traces to locate where duplication occurs.
MediumTechnical
89 practiced
Write a SQL query (Postgres-compatible) to find users whose daily transaction volume increased by more than 100% compared to their previous 7-day moving average. Given table: transactions(id, user_id, amount, occurred_at). Provide a solution using window functions and explain assumptions you make about missing days and minimum sample sizes.
EasyTechnical
78 practiced
Describe how to use 'git bisect' to find the commit that introduced a regression when you have a failing test. Show the sequence of commands you would run and explain strategies for handling flaky tests during bisection.
MediumTechnical
72 practiced
Implement a Python program that reads a very large file of newline-separated JSON objects and counts occurrences of a specific field value in constant streaming memory. Explain how you would handle malformed JSON lines, performance considerations (buffering, C-based JSON parsers), and fallback strategies if the number of unique values is too large to hold exactly in memory.
MediumSystem Design
73 practiced
Design a caching strategy for a read-heavy, latency-sensitive web endpoint. Compare local in-process cache, distributed cache (Redis/Memcached), and CDN. Explain cache invalidation approaches (TTL, write-through, cache-busting), consistency trade-offs, and which metrics you would track to measure cache effectiveness (hit rate, origin load, latency).
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