Technical Depth and Current Knowledge Questions

Assessment of a candidate's deep technical expertise and up to date hands on knowledge across core engineering domains. Interviewers will probe system design, performance optimization, distributed systems patterns, databases both relational and non relational, caching strategies, messaging and queuing systems, application programming interfaces, cloud infrastructure, observability and monitoring, and relevant programming languages and runtimes. Candidates should be prepared to discuss concrete technical trade offs, debugging and performance tuning approaches, how they research unfamiliar topics to maintain accuracy, and examples of technical decisions they have owned. This topic covers maintaining current technical fluency even in leadership roles and being able to have rigorous technical discussions about architecture and implementation.

HardTechnical

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Compare distributed locking approaches: Zookeeper ephemeral znodes, etcd leases, and Redis-based (Redlock) algorithms. Discuss correctness (safety), liveness, clock drift impact, network partitions, and how to choose an approach for leader election and short-lived critical sections.

EasyTechnical

0 practiced

Explain strong consistency versus eventual consistency with concrete examples. For a social feed where eventual consistency is acceptable, describe techniques to reduce confusing user experiences (e.g., missing data, visible reordering) and when you'd instead require strong consistency.

EasyTechnical

0 practiced

Explain the basics of garbage collection in managed runtimes (e.g., JVM, .NET). What are young/old generations, GC pause implications for latency-sensitive services, and simple actions a Solutions Architect can recommend to mitigate GC-related latency issues before detailed profiling?

HardTechnical

0 practiced

Intermittent 500ms latency spikes are observed in a critical web service. Describe a step-by-step debugging plan covering data collection (metrics, traces, logs), hypothesis formation (GC, DB, network, thread contention), specific tools to use, how to correlate signals across layers, and how to communicate findings and mitigation steps to stakeholders.

HardTechnical

0 practiced

Explain how garbage collection (GC) pauses in a JVM-based microservice can impact end-to-end latency. Provide a plan to identify GC-related issues across a fleet, tools and logs to collect, specific GC tuning levers (collector choices, heap sizing, ergonomics), and architectural mitigations (pooling, heapsize tuning, off-heap).

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