Advanced interview questions in Growth & Experimentation, page 2
What data pipelines and infrastructure feed a viral user acquisition model?
Tests causal attribution architecture. Great answers cover invite instrumentation with identity resolution, streaming pipelines that split organic and viral signups, and feature stores for network-state features.
How would you adapt a growth model for network effects and k-factor?
Define K as invites x conversion; K over 1.0 explodes, yet K over 0.7 with fast cycle time still compounds; anchor at peak delight.
Describe a strategy for reconciling different forecasts into one robust prediction
Tests synthesis of heterogeneous models into a consensus forecast. Strong answers diagnose divergence drivers first, then weight by track record or uncertainty, and output a distribution. Red flag: blind averaging without understanding why models disagree.
Design a system to reduce large client-side experiment payload size
Tests edge evaluation and payload compression. Use server-side pre-evaluation or edge nodes sending only assigned variants; compact bucketing indexes or Bloom filters; lazy-load noncritical experiments. Never do full client-side evaluation of every flag rule.
Design a pre-aggregation architecture for low-latency experiment results
Tests OLAP-at-scale trade-offs. Strong answers design streaming rollups into a real-time OLAP store, use partial cubes for high-cardinality dimensions, and retain raw events.
How do you mitigate peeking in experiment infrastructure?
Lock results behind minimum samples; auto-correct via sequential testing; hide early metrics and require stop approval.
Compare server-side and client-side experimentation architectures
This tests rendering-layer architecture. A strong answer contrasts server-side zero-flicker and algorithm tests against client-side marketer agility and SEO safety, mapping each to release cycles. A red flag is claiming one approach dominates every dimension.
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