Testing Debugging and Instrumentation Questions

Testing strategies and observability practices for software and hardware systems, including embedded contexts. Topics include unit testing, integration testing, hardware in the loop testing, test harnesses, test automation, and trade offs when testing resource constrained systems. Instrumentation covers logging design, metrics, tracing, telemetry, and debug interfaces that make systems observable in development and production. Debugging techniques include use of debuggers, serial logging, signal capture, oscilloscope traces, remote debugging, and structured troubleshooting workflows. Discuss design decisions that balance visibility against performance and safety requirements, how to make systems testable and instrumented from the start, and how to interpret instrumentation to localize faults and validate fixes.

HardSystem Design

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Design an alerting automation system that creates tickets when SLO burn rate crosses thresholds, deduplicates related alerts across services, correlates alerts with recent deployments (commit id, service owner), and attaches suggested runbooks. Describe fingerprinting/deduplication strategy, ticket prioritization rules, and how to prevent alert storms from creating ticket floods.

MediumTechnical

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Write a SQL query against a PostgreSQL table logs(id PK, service text, timestamp timestamptz, level text, message text, labels jsonb) to find services that produced more than 1000 ERROR logs in the last 24 hours and return counts per service. Explain how you would optimize the query for performance with high ingestion rates.

EasyTechnical

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Write a Python function parse_log_line(line: str) -> dict that parses structured log lines produced as key=value pairs (e.g., ts=2025-01-01T12:00:00Z level=ERROR msg='disk full' code=500) and returns a dictionary with keys timestamp, level, message, and error_code (if present). The function should return None for malformed lines and normalize timestamp to ISO8601 string. Describe how you'd validate and unit test the parser.

MediumTechnical

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You receive a CPU usage alert for a service. Given metrics: CPU utilization 95th=85%, p95 latency=120ms, p99 latency=500ms, request rate=1000 rps, error rate=0.1%. Describe a structured troubleshooting approach using metrics, logs, and traces to find the cause and reduce CPU usage. Prioritize next steps and short-term mitigations versus long-term fixes.

EasyTechnical

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List practical techniques to make a service more testable from an SRE perspective (for example: dependency injection, abstractions for external calls, deterministic time sources, feature flags). For each technique give a short example showing how it reduces flakiness or lowers the cost of debugging in CI and production.

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