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Root Cause Analysis and Diagnostics Questions

Systematic methods, mindset, and techniques for moving beyond surface symptoms to identify and validate the underlying causes of business, product, operational, or support problems. Candidates should demonstrate structured diagnostic thinking including hypothesis generation, forming mutually exclusive and collectively exhaustive hypothesis sets, prioritizing and sequencing investigative steps, and avoiding premature solutions. Common techniques and analyses include the five whys, fishbone diagramming, fault tree analysis, cohort slicing, funnel and customer journey analysis, time series decomposition, and other data driven slicing strategies. Emphasize distinguishing correlation from causation, identifying confounders and selection bias, instrumenting and selecting appropriate cohorts and metrics, and designing analyses or experiments to test and validate root cause hypotheses. Candidates should be able to translate observed metric changes into testable hypotheses, propose prioritized and actionable remediation steps with tradeoff considerations, and define how to measure remediation impact. At senior levels, expect mentoring others on rigorous diagnostic workflows and helping to establish organizational processes and guardrails to avoid common analytic mistakes and ensure reproducible investigations.

HardSystem Design
22 practiced
System design / architecture (hard): Design an RCA platform for an SRE organization that ingests metrics, logs, and traces; supports interactive cohort slicing, hypothesis tracking, reproducible notebooks, experiment integrations, and automated guardrails (sampling checks, pre-registered analyses). Specify core components, data model, storage tiers, query patterns, scaling considerations to 100k services and 10 TB/day, and how you would enforce reproducibility and lineage.
EasyTechnical
20 practiced
Coding: Implement in Python a function parse_error_counts(log_lines: List[str]) -> Dict[str, Dict[str,int]] that parses log lines in this format: '[timestamp] service=PAYMENTS level=ERROR msg=...'. The function should return counts per service per level (e.g., { 'PAYMENTS': {'ERROR': 10, 'WARN': 2} }). Handle malformed lines gracefully and work in O(N) time where N is number of lines. Explain memory considerations when logs are large.
HardTechnical
22 practiced
Explain how to use funnel and customer-journey analyses together with cohort alignment to differentiate a product regression from an infrastructure regression. Provide an example where overall conversion drops but a particular step's latency increase reveals an infra cause; describe the exact slices, alignment windows, and telemetry cross-checks you'd perform.
EasyTechnical
24 practiced
Describe the 'Five Whys' root cause analysis technique and apply it to the following incident: the payment service returned HTTP 500 errors for 30 minutes, causing a 15% drop in successful transactions and visible customer complaints. Provide at least five iterative 'why' steps that map the symptom to an underlying systemic cause, explain how you would validate each step using logs/metrics/traces/config diffs, and describe situations where Five Whys is insufficient and you would escalate to other methods.
HardTechnical
24 practiced
Describe how to create reproducible RCA notebooks that combine metrics queries, log slices, and traces. What metadata, versioning, data snapshots, and permissions do you store to ensure others can re-run and validate the investigation later? Outline a practical workflow and storage design for reproducible investigations.

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