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.

EasyBehavioral

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Behavioral: Tell me about a time you discovered a small configuration change or single-line bug that could have caused, or did cause, a larger outage. Describe the situation using STAR: the situation, your task, the actions you took to investigate and remediate, and the outcome, including what you changed in process or automation afterward.

MediumBehavioral

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Describe how you turn lessons learned from post-incident reviews into lasting process or tooling improvements. Provide concrete examples: how you change runbooks, automate repetitive mitigations, update alerting thresholds, and measure whether the changes actually reduced recurrence or MTTR.

MediumSystem Design

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Design an automated incident triage system that ingests 10,000 alerts per day from multiple services and groups similar alerts, assigns priority, and suggests likely root causes. Describe components (ingest, normalization, correlation, machine-learning or rule-based grouping), data models, evaluation metrics, and how you would measure success and avoid noisy suggestions.

EasyTechnical

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Implement a Python function that computes the p95 (95th percentile) latency from a list of response times (in milliseconds). The function should handle large lists efficiently and clarify whether you can modify the input list. Also explain how you'd compute p95 on streaming data and in distributed processes.

MediumTechnical

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Compare simple moving-average detectors, exponentially weighted moving average (EWMA), median absolute deviation (MAD), and quantile-based detectors for time-series anomaly detection in production. For each method, discuss sensitivity to sudden shifts, seasonality, computational cost, and suitability for high-cardinality metrics.

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