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Error Handling and Defensive Programming Questions

Covers designing and implementing defensive, fault tolerant code and system behaviors to prevent and mitigate production failures. Topics include input validation and sanitization, null and missing data handling, overflow and boundary protections, exception handling and propagation patterns, clear error reporting and structured logging for observability, graceful degradation and fallback strategies, retry and backoff policies and idempotency for safe retries. Also address concurrency and synchronization concerns, resource and memory management to avoid exhaustion, security related input checks, and how to document and escalate residual risks. Candidates should discuss pragmatic trade offs between robustness and complexity, show concrete defensive checks and assertions, and describe test strategies for error paths including unit tests and integration tests and how monitoring and operational responses tie into robustness.

MediumTechnical
0 practiced
A production model serving cluster is seeing increased tail latency due to resource contention. Describe concurrency and timeout controls you would add at the serving layer, including worker pool sizing, request timeouts, queue limits, and backpressure mechanisms to prevent cascading failures.
MediumTechnical
0 practiced
Design a concise set of structured error codes and log fields for a prediction service (e.g., VALIDATION_ERROR, TIMEOUT, RESOURCE_EXHAUSTED, MODEL_NOT_LOADED). Explain how clients, monitoring systems, and on-call engineers would use these codes to triage problems efficiently.
MediumTechnical
0 practiced
Design a small runbook for on-call engineers to follow when a model-serving cluster begins returning frequent 500 errors or timing out. The runbook should include immediate mitigation steps, diagnosis commands, metrics to inspect, safe rollbacks, and communication templates for affected stakeholders.
MediumTechnical
0 practiced
Explain how to implement graceful degradation for a search or recommendation ML service when the model is overloaded: include prioritized request routing, returning cached results, using a lightweight heuristic model, and recording user-visible degradation metrics. How do you balance business objectives with system stability?
MediumTechnical
0 practiced
Explain the differences between logs, metrics, and traces for ML observability. For production error handling, which signals would you rely on for alerting vs for debugging, and how would you correlate them to investigate an inference-time failure?

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