Centers on system and solution design decisions and the trade offs inherent in architecture choices. Candidates should be able to identify alternatives, clarify constraints such as scale cost and team capability, and articulate trade offs like consistency versus availability, latency versus throughput, simplicity versus extensibility, monolith versus microservices, synchronous versus asynchronous patterns, database selection, caching strategies, and operational complexity. This topic covers methods for quantifying or qualitatively evaluating impacts, prototyping and measuring performance, planning incremental migrations, documenting decisions, and proposing mitigation and monitoring plans to manage risk and maintainability.
HardTechnical
31 practiced
In Python, implement a function reconcile_events(events, window_start, window_end) that aggregates event counts per 'key' within a time window while deduplicating events using 'event_id' and handling out-of-order timestamps. Sample input:\n\nevents = [\n {'event_id':'a1','key':'page','ts':100,'count':1},\n {'event_id':'a2','key':'page','ts':95,'count':1},\n {'event_id':'a1','key':'page','ts':100,'count':1} # duplicate\n]\n\nExpected output for window 90-110: {'page': 2}\n\nImplement efficient logic and explain how it bounds memory usage for very large streams.
HardSystem Design
26 practiced
Design a telemetry and observability framework for a BI platform to detect regressions such as increased query latency, incorrect aggregates, and stale data. Specify key metrics, logs, distributed tracing points, dashboards for SREs and analysts, alerting rules, and examples of automated remediations you might implement.
MediumTechnical
47 practiced
You're migrating a set of critical dashboards to a new BI platform. Propose a monitoring and alerting plan to detect regressions during rollout. Include SLOs to track, canary metrics, shadow-traffic strategies, automated comparisons between old and new outputs, and clear escalation paths for failures.
HardTechnical
32 practiced
As lead BI analyst you must decide whether to build an in-house analytics platform or purchase a SaaS solution. Provide a structured evaluation framework covering total cost of ownership (TCO), feature parity, vendor lock-in, security/compliance, time-to-value, integration effort, and team capabilities. Describe how you'd score options and present a recommendation to executives.
HardSystem Design
32 practiced
Design a multi-tenant embedded analytics service that serves dashboards for many customers with strong isolation and fair resource allocation. Discuss logical multi-tenancy versus dedicated infrastructure per tenant, per-tenant quotas and throttling, schema isolation, encryption options, and the operational costs and complexity of each model. Include trade-offs around scalability and isolation.
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