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Technical Vision and Infrastructure Roadmap Questions

This topic assesses a candidate's ability to define a multi year technical vision for infrastructure, platform, and systems and to translate that vision into a practical execution roadmap. Core skills include evaluating technology choices and architecture evolution, planning migration and modernization paths, anticipating scalability and capacity needs, and balancing cost performance with resilience and operational reliability. Candidates should demonstrate approaches to managing technical debt, sequencing investments across quarters and releases, estimating resources and timelines, establishing measurable infrastructure goals and key performance indicators, and implementing governance and standards. Discussion may also cover reliability and observability, security and compliance considerations, trade offs between short term stability and long term rearchitecture, prioritization to enable business outcomes, and communicating technical trade offs to both technical and non technical stakeholders.

EasyTechnical
0 practiced
Describe the core components of a modern data platform that supports analytics and ML. Cover ingestion, storage (hot/warm/cold), processing (batch/stream), serving/warehouse, metadata/catalog, governance, and self-serve tooling. For each component give one example technology (open-source or managed) and the primary design consideration when including it in a roadmap.
HardSystem Design
0 practiced
Architect an observability and alerting system that spans microservices, batch jobs, and streaming pipelines. Define SLI choices per workload, how to correlate traces across services and pipelines, strategies to reduce alert noise (aggregation, severity levels), storage and retention trade-offs, and tools/UX for fast root cause analysis at scale.
HardTechnical
0 practiced
Assess adopting Kubernetes and containerization for stateful data services (Kafka, Spark, Cassandra) at enterprise scale. Discuss operational implications, storage choices (local disks, network-backed PVs, CSI drivers), operator maturity, upgrade and failure modes, stateful vs stateless trade-offs, and compare to using managed cloud services.
MediumSystem Design
0 practiced
Design a multi-region replication strategy for a data platform serving global users. Discuss synchronous vs asynchronous replication, region-local read strategies, eventual consistency impacts on analytics, cross-region bandwidth and cost, and a recommendation for serving low-latency queries versus global analytics workloads.
HardSystem Design
0 practiced
Design a backup and disaster recovery (DR) strategy for a globally distributed data lake containing both hot (recent) and cold (archival) data. Specify RTO and RPO targets for different data classes, cross-region replication approach, backup frequency and retention, restore drills, and cost trade-offs. Include automation for failover and data integrity verification.

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