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.
MediumTechnical
36 practiced
Explain how you'd implement runtime data assertions in a pipeline. Example assertions: 'user_id NOT NULL', 'email matches regex', 'timestamp within last 30 days'. Where in the pipeline would you run these validations (ingest, transform, sink), how would you handle failing records (reject, quarantine, alert), and how would you surface assertion failures to developers with actionable diagnostics?
HardSystem Design
31 practiced
Design an automated regression-detection system for production data quality anomalies. The system should monitor many metrics per pipeline (for example: row counts, null rates, distribution sketches), detect regressions relative to historical baselines, and surface prioritized alerts to engineers. Describe feature selection, baseline modeling (control charts, seasonal decomposition), ML-based anomaly models, thresholding to reduce false positives, and a feedback loop to incorporate human labels.
HardTechnical
49 practiced
Write a concise, structured postmortem for a hypothetical incident: a schema change was deployed to production without a backfill; downstream machine learning models consumed missing fields and produced erroneous predictions for 3 days. The postmortem should include timeline, detection, root cause, contributing factors, impact (quantify if possible), mitigation steps taken, long-term remediation, and validation plan to ensure the issue is resolved.
MediumSystem Design
34 practiced
Design an observability platform tailored for data pipelines that provides unified logs, metrics, lineage, and traceability. Requirements: support 10,000 pipelines, petabyte datasets, searchable logs for the last 90 days, near-real-time alerting (<1 minute), and per-record lineage lookup for the last 30 days. Describe architecture components, data flow, storage choices (for example: Kafka, ClickHouse, Elasticsearch, S3), indexing strategies, sampling strategies, and cost-performance trade-offs.
EasyBehavioral
27 practiced
Tell me about a time when you debugged a complex data pipeline failure under time pressure. Use the STAR method (Situation, Task, Action, Result). Explain how you prioritized actions, communicated with stakeholders, what diagnostic tools or queries you used, how you validated your fix, and what long-term preventative measures you implemented.
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