Technical Depth and Domain Expertise Questions

Covers a candidate's deep hands on technical knowledge and practical expertise in one or more technical domains and their ability to provide credible technical oversight. Interviewers probe specialized system design, domain specific patterns and constraints, and how the candidate stays current in the field. Expect questions on platform internals such as Linux and Windows internals, networking fundamentals including transport and internet protocols, domain name system, routing, and firewalls, database internals and performance tuning, storage and input output behavior, virtualization and containerization, cloud infrastructure and services, application performance analysis, security principles, and troubleshooting methodologies. Candidates should be prepared to explain architecture and design trade offs, justify technical decisions with metrics and benchmarks, walk through root cause analysis and debugging steps, describe tooling and automation used for deployment and operations, and discuss capacity planning and scaling strategies. For senior roles, demonstrate both breadth across multiple domains and depth in one or two specialized areas with concrete examples of diagnostics, performance tuning, incident response, and technical leadership. Interviewers may also ask why the candidate specialized, how they built that expertise, how that expertise shaped technical decisions and trade offs in real projects, expected failure modes and performance considerations, and how the candidate mentors others or drives best practices within their specialization.

HardTechnical

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You need to trace an intermittent slow inference path that appears to involve kernel syscalls. Describe how you would use eBPF and perf to instrument the system, what events and histograms you would collect, and how you would build flamegraphs or latency heatmaps to correlate syscall durations with user-space stacks and network events.

HardTechnical

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Explain how gradient accumulation interacts with optimizer state and learning rate scheduling in mixed-precision training across both data-parallel and pipeline-parallel setups. Discuss numerical stability concerns, loss-scaling strategies, how accumulation steps affect effective batch size and learning rate, and checkpointing considerations to resume correctly.

MediumTechnical

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You run multi-GPU training on a Kubernetes cluster with GPU nodes. Explain how you'd schedule multi-GPU jobs to optimize utilization and avoid GPU fragmentation. Discuss node labeling, kube-device-plugin, topology-aware scheduling, gang-scheduling, resource requests/limits, and how to handle mixed GPU types.

EasyTechnical

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List and explain the most important metrics to collect for a production inference service to diagnose performance and correctness issues. Include resource metrics, latency histograms, throughput, error rates, input feature distributions, model confidence distributions, and data-quality indicators. Explain how these metrics help detect both performance regressions and model drift.

EasyTechnical

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Compare horizontal (scale-out) and vertical (scale-up) scaling strategies for machine learning workloads. Provide concrete examples for when to use each for batch training versus real-time inference, and discuss trade-offs in cost, fault tolerance, and operational complexity.

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