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.
HardTechnical
0 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.
MediumTechnical
0 practiced
Explain common numerical pitfalls in ML code (softmax overflow/underflow, log(0), catastrophic cancellation, gradient explosion/vanishing). For each, describe defensive coding patterns and tests to catch them (use of log-sum-exp, clamping, gradient clipping, finite-value assertions). Provide a short Python code sketch for a numerically stable softmax implementation.
MediumTechnical
0 practiced
Design tests to ensure a preprocessing pipeline preserves label alignment when shuffling, batching, and augmenting data. Provide unit-test style cases using small synthetic datasets with known mappings and duplicate labels. Describe assertions and random-seed strategies to detect misalignment introduced by parallel or streaming loaders.
MediumTechnical
0 practiced
Propose a strategy to generate synthetic test datasets that exercise rare edge cases in tabular features: extreme outliers, structured missingness, skewed distributions, high-cardinality categoricals, and correlated features. Include sampling and parameterization strategies, seeding for reproducibility, and storage/versioning approach for test artifacts.
MediumTechnical
0 practiced
Implement a simple Python fuzz test for an image preprocessing function preprocess_image(bytes_input) that tries random byte mutations (truncation, bit flips, insertion) and asserts the function either raises a known exception or returns a valid tensor with expected shape/dtype. Show how to log the reproducer bytes and minimal reproduction steps when a crash occurs.
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