Backend Language Proficiency Questions

Demonstrate strong practical knowledge of a backend programming language such as JavaScript with Node dot js, Python, Java, Go, or similar. Cover language fundamentals, idiomatic usage, standard library features, package and dependency management, common frameworks and ecosystem tools, testing strategies and tooling, error handling and observability, and patterns for maintainable server side code. Be ready to explain concurrency and asynchronous models in the language, performance considerations, security best practices, deployment and packaging approaches, and examples of backend services or APIs you built including trade offs and chosen libraries.

MediumSystem Design

33 practiced

Design a low-latency model serving API that must handle 100 requests per second with a 50ms P95 latency SLO. Define the API contract, choice between synchronous and asynchronous models, batching strategy, concurrency model, hardware assumptions (CPU/GPU), caching, monitoring, and graceful degradation strategies. Justify your choices as an AI backend engineer.

HardTechnical

42 practiced

Explain techniques to serve GPU-based models across multiple processes and servers: CUDA context isolation, launching multiple processes versus multi-threading with a single context, inference runtimes such as TensorRT or ONNX Runtime, NCCL for distributed multi-GPU, and trade-offs between throughput, latency, and memory duplication.

EasyTechnical

68 practiced

Explain how goroutines in Go differ from OS threads in terms of scheduling, memory footprint, and stack growth. Describe how the Go runtime multiplexes goroutines onto OS threads and give an example where goroutines are a superior choice for concurrent preprocessing of data for an AI pipeline.

EasyTechnical

38 practiced

Describe the Node.js event loop phases and how asynchronous callbacks are scheduled. Explain the differences and ordering between process.nextTick, Promise microtasks, setImmediate, and setTimeout(fn, 0). Give an example scenario in a backend inference service where choosing the wrong scheduling API could cause starvation or increased latency.

MediumTechnical

39 practiced

Outline how you would instrument a backend API to provide distributed tracing across services implemented in Python, Java, and Go. Explain context propagation mechanisms, span tagging for model metadata (model id, version, input size), sampling strategies, and how traces would help diagnose high-latency model calls and resource contention.

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