Product Knowledge Foundation Questions

Baseline understanding of the company and its primary product or service: what problem it solves, who the users or customers are, the product value proposition, key features and capabilities, major components and high level technical architecture, and how it competes in the market. Candidates are expected to have researched the product enough to clearly summarize its purpose, target users, core workflows, and business goals, and to explain at a basic level how the technology and integrations enable those outcomes. Interviewers use this to assess research preparation, domain comprehension, ability to synthesize product information, and clear communication of product value rather than deep technical expertise.

HardSystem Design

84 practiced

You must rewrite a critical backend service in a new language/framework with zero downtime and no data loss. Outline a migration plan: compatibility layer approach, dual-writing or read-routing strategies, feature flag gating, data migration steps, verification tests, progressive cutover plan, and rollback options if problems occur.

HardTechnical

134 practiced

Design an automated incident response workflow that integrates monitoring alerts, runbook automation, and chatops actions for common situations (service degradation, DB latency spike). Describe required integrations, safe guardrails (e.g., rate limits on automated actions), escalation paths, and how to audit automated responses to avoid causing further harm.

EasyTechnical

91 practiced

You join as a new SRE with limited product knowledge. Outline a 30-day learning and impact plan that covers: which stakeholders you will meet (PMs, support, engineering), which metrics/dashboards and runbooks to review, which customer issues to read, and one quick operational improvement you could deliver within 30 days.

HardTechnical

93 practiced

The product projects 10x user growth over the next 12 months. Create a capacity planning and performance strategy: describe forecasting methodology (traffic patterns, headroom), load-testing approach (kinds of tests, environments), autoscaling policies, buffer planning for peaks, and the instrumentation you need to validate assumptions during growth.

MediumTechnical

143 practiced

Long-tail bugs affect a small subset of users and are costly to store detailed traces for. Propose observability techniques (adaptive sampling, dynamic trace sampling, structured logs with context IDs, feature-flag tagging) to detect and debug rare issues without exploding telemetry costs. Explain trade-offs and detection strategies.

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