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.

HardTechnical

0 practiced

You are tasked with driving adoption of reproducible RCA practices across multiple product teams. Propose a rollout plan, key success metrics (e.g., time-to-diagnosis, percent of RCAs with lineage), training curriculum, and approaches to handle teams that prefer ad-hoc analysis. Explain short-term wins that would help adoption.

EasyBehavioral

0 practiced

Tell me about a time you led or contributed to a root cause analysis for a meaningful business or product metric regression. Describe the situation, how you generated hypotheses, validation steps you ran with data, stakeholders you collaborated with, the remediation that was implemented, and the measurable outcome.

HardSystem Design

0 practiced

Design a metric lineage and validation system to prevent 'metric drift' where definitions change silently. Describe how you'd implement metric versioning, test suites for metric calculations (unit and end-to-end), CI gating for changes, and discoverability for analysts (registry, owners, changelog).

EasyTechnical

0 practiced

Given the events table below (Postgres):

events(
  event_id serial,
  user_id int,
  event_type text,
  event_time timestamptz
)

Write a SQL query to compute Daily Active Users (DAU) for the last 30 days and the week-over-week percent change compared to the same weekday one week prior. Show output columns: date, dau, wau_change_pct. Explain assumptions about timezones and how you would treat days with zero activity.

EasyTechnical

0 practiced

Explain cohort analysis in product analytics. Provide an example of how you'd compute 7-day retention for weekly acquisition cohorts and discuss how cohort size, cohort granularity (daily vs weekly), and user reactivation affect interpretation of the retention curve.

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