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Solution Architecture and Design Questions

Designing and architecting end to end technical solutions for enterprise and complex systems, covering both the methodology for approaching architecture problems and the practical component level design work. Candidates should demonstrate a repeatable structured approach to elicit and document functional and non functional requirements, identify constraints and stakeholders, evaluate and compare multiple architectural options, and justify technology choices. They should produce high level and component level designs that show major services, presentation layers, application tiers, data layers, data flows, storage strategies, application programming interfaces, integration points with external and third party systems, and data movement and transformation. Strong responses explicitly address quality attributes such as scalability, performance, availability, fault tolerance, reliability, consistency and security as well as compliance and data protection concerns. Operational concerns must be covered including deployment topology, multi region and hybrid cloud strategies, monitoring and observability, logging, capacity planning, backup and disaster recovery, deployment and release strategies, maintenance, and operational run books. Candidates should discuss communication patterns including synchronous remote procedure calls and asynchronous messaging, storage trade offs between relational and non relational datastores and data warehouses, failure modes and mitigation strategies, incremental evolution and migration paths, and cost and feasibility constraints. Interviewers assess the ability to present clear diagrams, explain interactions and failure modes, reason about trade offs, and justify design decisions against requirements and constraints.

MediumTechnical
17 practiced
Describe possible failure modes when the upstream data warehouse becomes unavailable and the impact on dashboarding. Propose mitigation strategies: degraded UX (cached snapshots), read-only fallbacks, alerting/communication plans, and how to prioritize mitigations for executive dashboards versus analyst exploration.
EasySystem Design
31 practiced
Sketch a high-level component architecture for a self-service BI platform used by analysts and business users. Include data sources, ingestion/connector layer, semantic/metrics layer, query/compute layer, cache, visualization layer (e.g., Power BI/Tableau/Looker), embedding/API layer, and access control. For each component, state its primary responsibilities and point of failure.
MediumSystem Design
21 practiced
Explain integration patterns for embedding BI dashboards into a multi-tenant SaaS application: iframe embedding with SSO, SDK-based embedding, and server-side rendering of images/PDFs. For each pattern, discuss authentication propagation, data scoping for tenants, security considerations, and performance impact on the host application.
MediumBehavioral
19 practiced
Tell me about a time you influenced engineering and product teams to adopt a centralized semantic metrics layer to reduce inconsistent KPIs. Explain how you identified the problem, built consensus, handled pushback, measured adoption, and quantified the impact on reporting accuracy or user trust.
HardTechnical
18 practiced
Create an outline for runbooks and on-call playbooks tailored to BI incidents such as data freshness lag, corrupted materialized views, and failed scheduled reports. For each incident type provide detection signals, immediate mitigation steps, rollback procedures, stakeholder communication templates, and post-incident actions.

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