Systematic Troubleshooting and Debugging Questions
Covers structured methods for diagnosing and resolving software defects and technical problems at the code and system level. Candidates should demonstrate methodical debugging practices such as reading and reasoning about code, tracing execution paths, reproducing issues, collecting and interpreting logs metrics and error messages, forming and testing hypotheses, and iterating toward root cause. Topic includes use of diagnostic tools and commands, isolation strategies, instrumentation and logging best practices, regression testing and validation, trade offs between quick fixes and long term robust solutions, rollback and safe testing approaches, and clear documentation of investigative steps and outcomes.
HardTechnical
33 practiced
Implement a robust strategy to diagnose and mitigate a production-wide database connection pool exhaustion that occurs under high load. Describe checks at application, driver, and database levels (e.g., checking pool metrics, pg_stat_activity), preventive measures (connection pool tuning, circuit breakers, request queueing), and a safe plan to reduce the number of active connections without downtime.
EasyTechnical
38 practiced
Explain what a correlation ID (request ID) is and how you would instrument a distributed system to propagate it across services, asynchronous queues, and logs/traces. Describe how correlation IDs help with systematic debugging and list common pitfalls (e.g., missing propagation, high-cardinality explosion, privacy leaks). Give examples of headers and language/framework-specific approaches you might use.
MediumTechnical
32 practiced
Write a Jenkinsfile (Declarative Pipeline) snippet that builds a Docker image, runs unit tests inside the image, archives test results and artifacts on failure, pushes the image to a private registry on success, and deploys to a Kubernetes namespace named 'staging'. Show how you would securely reference credentials and how you would collect logs/artifacts for failed builds.
HardSystem Design
33 practiced
Design an observability and instrumentation plan (logs, metrics, traces, health checks) for a new payment service that must be debugged end-to-end under production traffic of 1,000 TPS. Include sampling strategies for traces, log indexing and retention decisions, which high-cardinality fields to index or avoid, alerting thresholds, and cost vs debugability trade-offs, including compliance concerns for sensitive data.
HardTechnical
49 practiced
Given a C++ native process inside a container that intermittently crashes with SIGSEGV only in production, describe how you would obtain a reproducible test case, safely collect and analyze core dumps from the container, map crash addresses to source code when binaries are stripped, and determine whether the issue is memory corruption, a stack overflow, or an ABI/library mismatch.
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