AI & Technical question
Replit wants optimization based on trustworthy conversion signals, not noisy platform defaults. Define the event taxonomy, identity and attribution approach, and client-side/server-side instrumentation you’d require from signup through retained activation. How would you handle latency, deduplication, audience exclusions, and warehouse reconciliation so bidding systems can optimize in near real time?
- Replit
- AI & Technical
- Hard
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What this question tests
Tests technical instrumentation design for trustworthy conversion signal that ad platforms can optimize against in near real time.
How to approach it
- Define an event taxonomy anchored on true conversion milestones, signup, first project run, and retained activation at a set day threshold, not vanity events.
- Design identity resolution that stitches anonymous ad click through to authenticated user, using a durable identifier that survives across sessions and devices where possible.
- Combine client side events for fast signal with server side events for accuracy and resilience to ad blockers, sending both to the ad platforms.
- Handle latency by sending early proxy events, like signup, quickly while backfilling the platform with the stronger retained activation signal once available.
- Deduplicate events between client and server pipelines using a shared event ID, and apply audience exclusions to suppress users who already converted from further spend.
- Reconcile warehouse truth against what was sent to ad platforms on a regular cadence, catching drift before it silently corrupts bidding decisions.
What a strong answer includes
- Separates fast proxy signals from the true retained activation signal explicitly, and describes backfilling the platform once the stronger signal is available.
- Combines client and server side tracking deliberately for resilience, rather than relying on one method alone which is vulnerable to blockers or SDK issues.
- Includes a concrete reconciliation step against the warehouse, which is what actually catches instrumentation drift before it corrupts bidding decisions.
Common mistakes
- Optimizing bidding systems against a single early funnel event without ever correcting toward the true retained activation signal.
- Skipping deduplication between client and server events, which would inflate conversion counts and mislead the bidding algorithm.
Likely follow-up questions
- How would you handle a user who converts on one device but was acquired on another?
- What would you do if server side event volume and warehouse counts start diverging?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture