Problem Solving and Analytical Thinking Questions

Evaluates a candidate's systematic and logical approach to unfamiliar, ambiguous, or complex problems across technical, product, business, security, and operational contexts. Candidates should be able to clarify objectives and constraints, ask effective clarifying questions, decompose problems into smaller components, identify root causes, form and test hypotheses, and enumerate and compare multiple solution options. Interviewers look for clear reasoning about trade offs and edge cases, avoidance of premature conclusions, use of repeatable frameworks or methodologies, prioritization of investigations, design of safe experiments and measurement of outcomes, iteration based on feedback, validation of fixes, documentation of results, and conversion of lessons learned into process improvements. Responses should clearly communicate the thought process, justify choices, surface assumptions and failure modes, and demonstrate learning from prior problem solving experiences.

HardTechnical

0 practiced

A client requires near-zero RTO and RPO for a critical database. Propose an architecture across regions that meets this requirement, weighing synchronous versus asynchronous replication, the impact on write latency, failover choreography, consistency guarantees, and the testing plan to validate both failover and failback procedures.

EasyTechnical

0 practiced

Explain common time and space complexity classes including O(1), O(log n), O(n), O(n log n), O(n^2), and O(2^n). Give one architecture-level example where algorithmic complexity directly impacts system design or cost, and explain how you would mitigate complexity in that scenario.

MediumTechnical

0 practiced

Design an experiment to validate a suspected bug fix that will be rolled out to 5% of traffic. Define metrics to monitor, required sample size or statistical approach, threshold-based rollback criteria, how to route traffic safely, observability needed to detect regressions, and how to minimize user impact during the validation window.

HardTechnical

0 practiced

Design a near-real-time deduplication system for streaming events at millions per second with bounded memory. Compare algorithms such as Bloom filters, count-min sketch with decay, and sliding-window exact dedupe. Provide pseudocode for a scalable approach, estimate false positive rates vs memory, and discuss downstream impact and mitigation strategies for false positives and false negatives.

EasyTechnical

0 practiced

Given a stream of request latencies represented as a list of integers in milliseconds, implement a sliding-window moving average in Python that runs in O(n) time and uses O(1) additional memory beyond input and output. Example: input [100,200,300,400], window=3 => output [200]. State how you handle window larger than input length and null values in the stream. Pseudocode is acceptable.

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