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 team suspects an SSH key was compromised and used to access production instances. Walk through an incident response plan: containment steps, evidence collection and forensics, secret rotation strategy, communication and legal considerations, and longer-term mitigations to prevent reoccurrence. Be specific about preserving logs and avoiding loss of forensic data.

EasyTechnical

0 practiced

Define SLI, SLO, and SLA in the context of SRE. Explain how error budgets work and give a concrete example: propose SLIs and an SLO for a login API used by customers that must balance reliability and deployment velocity.

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.

HardTechnical

0 practiced

You need to evaluate whether a patch reduced error rates for a rare failure (say baseline 1 error per 10,000 requests). Explain a statistical testing plan: which test to use (Poisson, binomial), how to compute required sample size or test duration for a desired power, how to handle low counts and zero-inflation, and how to control Type I and Type II errors when stakes are high.

EasyTechnical

0 practiced

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.

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