Covers writing clean, maintainable, and readable code together with proactive techniques to prevent failures and handle unexpected inputs. Topics include naming and structure, modular design, consistent style, comments and documentation, and making code testable and observable. Defensive practices include explicit input validation, boundary checks, null and error handling, assertions, graceful degradation, resource management, and clear error reporting. Candidates should demonstrate thinking through edge cases such as empty inputs, single element cases, duplicates, very large inputs, integer overflow and underflow, null pointers, timeouts, race conditions, buffer overflows in system or embedded contexts, and other hardware specific failures. Also evaluate use of static analysis, linters, unit tests, fuzzing, property based tests, code reviews, logging and monitoring to detect and prevent defects, and tradeoffs between robustness and performance.
MediumTechnical
22 practiced
Write a robust Python function load_model(path) that loads a serialized model and handles errors clearly: file not found, unsupported format, version mismatch, checksum failure, and deserialization exceptions. Define a clear exception hierarchy (e.g., ModelNotFoundError, ModelCorruptError) and logging messages. Explain why silent failures are dangerous in ML systems.
HardTechnical
27 practiced
A deployed model sometimes returns NaN predictions under unknown circumstances. Walk through a systematic debugging plan: reproduce locally with logged inputs, add defensive logging and guards, instrument intermediate activations to detect NaN origin, check preprocessing and data distributions, examine gradients and optimizer state, and rollback to last stable model. Provide diagnostic code snippets and the tests you would add to prevent recurrence.
HardSystem Design
23 practiced
You suspect intermittent inference errors in production that are hard to reproduce. Design a chaos engineering experiment to surface latent reliability issues (GPU driver faults, memory pressure, network partitions, feature store timeouts). Define hypothesis, controlled fault injection steps, observable metrics, safety guards, and rollback criteria for the experiment.
HardTechnical
25 practiced
Explain how to make ML experiments reproducible across dev workstations, CI, and multi-GPU clusters. Cover random seeds (numpy, framework, system), deterministic operations flags in frameworks, dependency pinning, containerization (Docker), hardware-induced nondeterminism, and artifact tracking. Propose a reproducibility checklist and small tests to validate that two runs produce comparable artifacts.
HardTechnical
23 practiced
Design a runtime data validation service that performs lightweight checks at inference time and deeper asynchronous validation offline. Discuss schema enforcement, fast statistical checks, handling unseen categorical values, feature hashing collisions, and how to record and react to anomalies without exceeding latency budgets. Provide algorithmic outlines for both fast checks and offline aggregation.
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