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.

HardTechnical

36 practiced

You inherit a Python production service with many third-party dependencies, flaky tests, and frequent regressions. Propose a prioritized remediation plan to improve code quality, reduce test flakiness, and increase release confidence. Cover dependency hygiene, test determinism, CI changes, observability improvements, and incremental refactoring strategies.

MediumTechnical

55 practiced

Compare serialization strategies for model inputs and outputs to minimize latency: JSON, Protobuf, FlatBuffers, and raw binary. For a high-throughput endpoint handling many small structured prediction requests, which format would you choose and why? Discuss parsing CPU cost, memory copies, and schema evolution considerations.

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.

HardTechnical

37 practiced

Sketch a Java gRPC service that implements a server-side streaming RPC for model scoring where the client streams input records and the server sends an aggregated result when the stream completes. Discuss non-blocking IO runtime choices (Netty), threadpool sizing, backpressure and flow-control semantics, and resource cleanup on client cancellation.

EasyTechnical

36 practiced

You must choose between JSON and Protobuf for internal microservice communication that exchanges large model metadata and batched inputs. Compare them in terms of wire size, parsing speed, schema evolution, tooling, and developer ergonomics. Which would you prefer for high-throughput, low-latency internal RPC and why?

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