Intermediate interview questions in Growth & Experimentation, page 3
Describe cluster or switchback randomization for network-effect A/B tests.
Tests SUTVA violation, cluster-switchback tradeoffs. Outline: cluster (geo, teams) or switchback (time slices) isolation; cover pipeline changes, 10x inflation, and correlated error. Red flag: user-level randomization with post-hoc fixes or ignoring spillover.
Design a referral system: data models, APIs, attribution, self-referral prevention
Tests data modeling with fraud guardrails and idempotent rewards. Cover: Users with nullable referred_by, ReferralEvents state table, async ledger attribution, and device-fingerprint self-referral blocks. Red flag: bare integer credit with no audit trail.
Build a system to measure viral coefficient and attribute invites to signups
Tests if you can map K=i×c to logged events and resilient pipeline. Good answers define invite_sent, click, signup events with referral tokens; sketch stream joins; and flag cross-device and organic attribution gaps. Red flag: assuming perfect attribution.
Explain the difference between statistical and practical significance
Define statistical vs practical significance; note large samples make tiny effects significant; give a real example.
Design a referral feature's lifecycle and races
A referral entity with explicit states, a unique constraint on the invited user, and atomic transactions plus idempotency to prevent double credits.
Run concurrent experiments without interference
Independent non-interacting tests can share traffic through orthogonal layers; interacting ones need mutual exclusion in a shared layer.
Design a contamination-safe pricing experiment
User-level price tests leak via fairness perception, so use geo holdouts or time-based cohorts where everyone in a unit sees one price.
Prevent conflicting experiments with layers
Group conflicting experiments into one layer so a user's per-layer bucket maps to at most one of them; orthogonal layers can overlap.
Increase experiment velocity for non-engineers
Server-driven config, feature flags, and a self-serve UI let non-engineers ship copy or layout variants instantly; add guardrails and metric checks.
Architect a configurable, goal-based onboarding flow
Capture the goal, let the backend return a server-driven flow definition mapping goal to steps and content, render generic components on the client.
Why repeatedly extending a test inflates false positives
Repeatedly checking and extending until significance is p-hacking via optional stopping, which inflates the false-positive rate; fix with fixed sample sizes or sequential…
Handle interaction effects on a shared page
Combined variants may produce effects neither has alone; use mutual exclusion for likely interactions, orthogonal designs with interaction monitoring otherwise.
Attribute a mobile install to a desktop ad
Deterministic matching via a shared login is accurate but needs auth on both ends; probabilistic fingerprinting scales without login but is noisy and privacy-fraught.
Set up a client-side button color A/B test
Stable hashing of a persistent ID into buckets, conditional rendering of the variant, exposure plus click event logging.
Design a centralized experimentation service
A config/assignment API, deterministic SDK-side bucketing, and a separate exposure-logging pipeline.
Experiment design under network effects
Cluster-level randomization, graph or geo clustering to contain spillover, and analysis at the cluster unit.
Client-side vs server-side event tracking
Client captures UI intent but loses data to ad blockers and tampering; server is trustworthy for transactions but blind to UI interactions.
Design an analytics event schema
Consistent object-action naming, snake_case, typed properties with units, and shared context like user, session, timestamp.
Migrate a breaking analytics schema change
Dual-write both fields during overlap, backfill history, migrate consumers, then deprecate the old field.
Design a streak-saver mechanism
Timezone-aware streak state, capped saver inventory with replenish rules, and A/B testing forgiveness against retention plus guardrails.
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