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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.

HardTechnical
0 practiced
A partial feature rollout via feature flags is causing inconsistent behavior for ~10% of users. Describe end-to-end diagnostic steps you would take across client SDKs, backend feature-flagging service, CDN caches, and targeting logic. Specify what logs and events you would collect, how you'd correlate client and server traces, and how to determine whether the issue is rollout logic, targeting, or client-side SDK mismatch.
HardTechnical
0 practiced
Duplicate user identities across devices are inflating DAU and underestimating retention. Propose detection heuristics to identify duplicate users, describe deterministic and probabilistic linking strategies, outline steps to clean historical data (merges vs soft-mapping), and discuss risks of incorrectly merging identities. How would you validate the corrected metrics?
HardTechnical
0 practiced
Design a company-level RCA playbook and guardrails for an organization of roughly 200 engineers and 25 PMs to ensure investigations are reproducible and analytically sound. Include required artifacts, roles, expected timelines, tooling (notebooks, dashboards), peer review steps, and explicit guardrails to avoid common analytic mistakes such as p-hacking or data leakage.
HardTechnical
0 practiced
A bug in the analytics ETL caused revenue events to be dropped for the last 72 hours. As PM, outline the steps you would take to: 1) quantify the missing revenue, 2) decide whether to backfill the missing data, 3) prioritize which downstream dashboards and reports must be recomputed, and 4) verify that the backfill is correct. Mention trade-offs of immediate backfill vs delayed recompute.
MediumTechnical
0 practiced
Design the instrumentation schema required to diagnose checkout failures across web and mobile. Specify event names, required attributes (for example payment_type, error_code, cart_value, device_os, sdk_version, session_id), sampling decisions, and how you would ensure idempotency and support for replay/backfill when downstream schemas change.

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