Focuses on systematically finding, reasoning about, and testing edge and corner cases to ensure the correctness and robustness of algorithms and code. Candidates should demonstrate how they clarify ambiguous requirements, enumerate problematic inputs such as empty or null values, single element and duplicate scenarios, negative and out of range values, off by one and boundary conditions, integer overflow and underflow, and very large inputs and scaling limits. Emphasize test driven thinking by mentally testing examples while coding, writing two to three concrete test cases before or after implementation, and creating unit and integration tests that exercise boundary conditions. Cover advanced test approaches when relevant such as property based testing and fuzz testing, techniques for reproducing and debugging edge case failures, and how optimizations or algorithmic changes preserve correctness. Interviewers look for a structured method to enumerate cases, prioritize based on likelihood and severity, and clearly communicate assumptions and test coverage.
HardTechnical
0 practiced
Design a fuzz-testing harness for a model inference API that accepts JSON payloads of features. Include example malformed input categories to generate: missing keys, additional unknown keys, wrong data types, numeric strings, extremely long strings, nested arrays where scalars expected, Unicode and control characters. Show 6 example JSON payloads that a fuzzer might generate, and describe how you would detect and triage failures (exceptions, silent incorrect predictions, latency spikes).
HardTechnical
0 practiced
Case study: In production, a library upgrade coincided with an 8% drop in model AUC. Walk through an end-to-end triage plan: how to detect the responsible change, reproduce the regression locally, decide whether to roll back, and add regression tests to prevent recurrence. Include specific steps like environment pinning, pip freeze, git-bisect or changelog review, deterministic retraining, and minimal failing test creation.
EasyTechnical
1 practiced
List typical off-by-one errors in time-windowed computations (for example inclusive vs exclusive endpoints, window definitions for rolling sums, and index-based slicing mismatches). Provide a concise unit test example (inputs and expected output) demonstrating an inclusive vs exclusive misunderstanding and how your test would fail for an incorrect implementation.
HardTechnical
0 practiced
You need to ensure that grouped results sorted by timestamp preserve a stable tie-breaker so downstream consumers see deterministic ordering. Describe properties you would test with a property-based testing framework: idempotence of sorting, stability on repeated runs, and invariants when timestamps are equal. Show one or two property ideas in plain pseudocode that would expose non-deterministic ordering bugs.
MediumTechnical
0 practiced
Design a fuzz-testing approach for a CSV ingestion pipeline that receives files from many partners. Consider encoding mismatches, different delimiters, quoted fields containing newlines, extremely long fields, corrupted bytes, missing headers, and mixed-type columns. Describe how to build an initial corpus, mutate inputs, run the harness, detect crashes and silent data corruption, and triage failures into actionable bugs.
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