Problem Solving and Structured Thinking Questions

Focuses on general problem solving strategies and structured thinking applicable to engineering, coding, and complex decision making. Core skills include clarifying the problem, breaking problems into subproblems, identifying patterns, selecting appropriate approaches and data structures, developing and testing incremental solutions, analyzing trade offs, reasoning about time and space complexity, handling edge cases, and communicating thought process clearly. Includes algorithmic patterns and design of systematic approaches to unfamiliar problems as well as frameworks for organizing thought under ambiguity.

HardSystem Design

0 practiced

Design a globally distributed quota and rate-limiting system that enforces per-user limits with low latency across regions. Discuss choice of consistency model (strong vs eventual), data stores, local caches, token bucket vs distributed counters, reconciliation after partitions, and how you would keep CPU and network overhead low.

MediumTechnical

0 practiced

Compare and evaluate caching strategies (LRU, LFU, TTL-based caches) for API response caching in a system where stale data is tolerated for 30 seconds but hit rate is critical. Propose a structured rubric to choose one strategy over another and how you'd benchmark the candidates in production-like conditions.

MediumTechnical

0 practiced

You must design a sliding-window rate limiter that supports 100k concurrent users and 10k requests/sec total. Describe appropriate data structures and algorithms, options to implement in-process vs Redis (or equivalent), memory and CPU trade-offs, and how to keep windows accurate with minimal locking and acceptable tail latencies.

EasyTechnical

0 practiced

You are on-call and receive an urgent alert: a critical public API endpoint reports a sudden spike in 500 errors and many users are impacted. Describe your structured, step-by-step approach to triage and mitigate this incident. Include what clarifying questions you ask, which logs/metrics/traces you gather first, how you form and rank hypotheses, immediate mitigations you might apply (e.g., traffic shaping, rollback, scaling), and criteria for escalation.

HardTechnical

0 practiced

Design an algorithm to merge latency histograms produced by N machines to compute a global p99 efficiently without storing raw samples. Describe the histogram representation, how you merge them, and analyze error bounds. Discuss trade-offs between fixed buckets (HDR) and sketch-based approaches (t-digest).

Unlock Full Question Bank

Get access to hundreds of Problem Solving and Structured Thinking interview questions and detailed answers.

Join thousands of developers preparing for their dream job.