Error Handling and Code Quality Questions

Focuses on writing production quality code and scripts that are defensive, maintainable, and fail gracefully. Covers anticipating and handling failures such as exceptions, missing files, network errors, and process exit codes; using language specific constructs for error control for example try except blocks in Python or set minus e patterns in shell scripts; validating inputs; producing clear error messages and logs; and avoiding common pitfalls that lead to silent failures. Also includes code quality best practices such as readable naming and code structure, using standard libraries instead of reinventing functionality, writing testable code and unit tests, and designing for maintainability and observability.

MediumTechnical

84 practiced

Write a Python decorator `exception_mapper(http_status_map: dict)` for a Flask-like endpoint that maps exception types to HTTP status codes and JSON bodies. The decorator should catch exceptions raised by the handler, look up the status, and return a JSON dict with keys `error`, `message`, and `code` while preserving the original exception message. Sketch the implementation (focus on mapping and response creation).

HardTechnical

106 practiced

Design metrics and alerts to proactively detect silent failures in model outputs, such as constant predictions, predictions with NaNs, or collapsed probability distributions. Include how you'd distinguish between model bugs and upstream data issues, and describe alert thresholds and on-call playbook steps.

EasyTechnical

75 practiced

You're writing shell scripts to prepare data for training. Explain the role of `set -e`, `set -u`, and `trap` for error handling in bash. Provide an example snippet that ensures the script exits on errors, treats unset variables as errors, and performs cleanup (deleting a temporary working directory) even when interrupted.

EasyTechnical

96 practiced

When should you use assertions (assert statements) versus explicit input validation in production ML code? Discuss the differences in intent, runtime behavior (e.g., python -O), and recommended uses (developer vs user input checks). Provide a concise guideline for an ML engineer.

MediumTechnical

73 practiced

Describe a testing strategy to make ML pipelines deterministic for unit tests: how to mock random seeds, external data sources, long-running training, and hardware-specific behavior (GPUs). Provide concrete techniques and code-testing patterns you'd use.

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