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.

EasyTechnical

21 practiced

What does MECE (mutually exclusive, collectively exhaustive) mean for hypothesis generation in RCA? Provide an example MECE hypothesis set for investigating a 15% drop in purchase conversions over the past 48 hours.

HardTechnical

25 practiced

Flaky tests are causing false-positive CI failures and noisy alerting. Design a pipeline to identify flakiness sources, automatically classify flaky tests (test-level, environment, timing, data), and prioritize fixes. Describe the telemetry you need and how you'd integrate fixes back into CI gating policies.

MediumTechnical

31 practiced

Given tables:

requests(request_id, service, timestamp, status)
dependencies(request_id, dep_service, dep_latency_ms, dep_status)

Describe a SQL or algorithmic approach to attribute increased 5xx errors in `requests` to a specific dependency by correlating time windows, latency spikes, and dependency failure rates. Be explicit about time-windowing and confidence checks.

EasyTechnical

22 practiced

Define 'instrumentation' in the context of diagnostics and observability. For a standard e-commerce web application, list at least 6 concrete metrics/events/spans you would instrument to enable future root cause analysis for checkout-related problems.

EasyTechnical

26 practiced

Given a Postgres table definition:

events(
  id UUID,
  service_name TEXT,
  status_code INT,
  occurred_at TIMESTAMPTZ
)

Write a SQL query to compute the daily error rate (status_code >= 500) per service for the last 7 days. Return columns: service_name, day, error_count, total_count, error_rate_percent.

Unlock Full Question Bank

Get access to hundreds of Root Cause Analysis and Diagnostics interview questions and detailed answers.

Join thousands of developers preparing for their dream job.