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.

EasyTechnical

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Explain model quantization. Compare post-training static quantization and quantization-aware training, outline how 8-bit quantization typically affects model accuracy and inference latency on CPUs and GPUs, and list when quantization is a suitable optimization and when it might not be appropriate.

MediumTechnical

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How would you tune PostgreSQL to maximize sustained high-throughput writes for an ML observability pipeline ingesting millions of small metric rows per second? Discuss configuration knobs such as shared_buffers, wal_level, synchronous_commit, checkpoint_timeout, wal_compression, autovacuum tuning, and filesystem considerations.

EasyTechnical

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Describe the differences between batch normalization and layer normalization: explain the normalization axes, dependence on batch statistics, behavior during training vs inference, and why transformers commonly use layer norm rather than batch norm. Give an example scenario where batch norm is preferable.

EasyTechnical

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Explain NVLink and how it differs from PCIe as a GPU interconnect. Discuss bandwidth, latency, peer-to-peer access semantics, unified-memory implications, topology considerations (e.g., NVSwitch), and the impact on multi-GPU distributed training and model-parallel tensor exchange patterns.

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