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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.

HardTechnical
97 practiced
Write a robust Python function to download a large model file (>10GB) from S3 using streaming, verify its SHA-256 checksum without loading the entire file into memory, write the file atomically to local disk (avoid partial files), and support retry with resume (range requests). Provide a code sketch that demonstrates key steps and error handling.
EasyTechnical
94 practiced
You have a deployment shell script that downloads model artifacts and restarts services. Describe how to make this Bash script fail-fast and handle partial failures. Specifically: explain `set -euo pipefail`, use of `trap` to cleanup on EXIT or ERR, handling exit codes of background processes, atomically writing downloads (`mktemp` + `mv`), and ensuring meaningful non-zero exit codes. Provide a short robust script snippet illustrating these patterns.
MediumSystem Design
72 practiced
Design approaches to handle partial failures in distributed training when one or more workers crash: describe checkpoint frequency trade-offs, master failover strategies, consistent optimizer-state recovery (e.g., for Adam), and options for synchronous vs asynchronous (stale) updates. Explain how to detect silent worker failures (e.g., stuck NCCL ops) and recover without corrupting model state.
HardSystem Design
81 practiced
Design a testing and automation strategy for data-drift detection and automated mitigation of production model degradation. Include statistical tests (KS, chi-squared) for feature-wise drift, baseline windows, thresholds for alerts, automated retraining pipelines with validation and canary evaluation, and measures to reduce false positives. Discuss human-in-the-loop checkpoints and rollback policies.
MediumSystem Design
74 practiced
Design a canary deployment and rollback strategy for model updates in production. Include traffic shifting, metrics for automated rollback (error rate, latency, model-quality delta), safe ramp rules, and how to stage model artifacts. Describe automated and manual rollback triggers and how to ensure data consistency and client compatibility during rollout.

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40+ Error Handling and Code Quality Interview Questions & Answers (2026) | InterviewStack.io