Metrics question
For partner-sold AI offerings where the cloud provider owns billing, how would you design pre-sale and post-sale fraud defenses against bot signups, mass registration, and chargebacks? Which shared signals would you require from each partner, and what outcome metrics would tell you the system is reducing fraud without unnecessarily hurting conversion or revenue?
- Anthropic
- Metrics
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests designing a fraud-defense system for a channel where the partner, not Anthropic, owns billing and the customer relationship, plus its success metrics.
How to approach it
- Map the fraud surface: pre-sale bot signups and mass registration, and post-sale chargebacks and stolen-card usage funneled through partner billing.
- Identify shared signals to request from each partner, such as account age and prior chargeback history.
- Layer on Anthropic-side signals, like usage velocity and content-pattern anomalies, that partners can't see.
- Design interventions proportional to risk, using soft friction like extra verification before hard blocks.
- Define outcome metrics: fraud rate per signup, chargeback rate, and false-positive rate on legitimate accounts blocked.
What a strong answer includes
- Sets a concrete guardrail, for example legitimate-account block rate must stay under half a percent as an illustrative target.
- Tracks detection and friction together as two metrics rather than optimizing fraud reduction alone.
- Designs around partial partner data visibility instead of assuming full signal access.
- Proposes a staged rollout, testing the fraud model on a subset before applying it broadly across partner billing.
Common mistakes
- Optimizing purely for fraud reduction and ignoring the conversion cost to legitimate signups.
- Assuming partners will share every signal needed without negotiating what's actually available.
Likely follow-up questions
- What would you do if a partner refuses to share chargeback data?
- How would you measure false positives when you can't always confirm ground truth?
More metrics questions
- What metrics define success for the Model Context Protocol (MCP) ecosystem?Anthropic · Metrics · Hard
- Design a KPI framework for Anthropic’s Human Data Platform that connects platform health to research outcomes. Which leading and lagging metrics would you track across time-to-launch, worker/vendor efficiency, data quality, and downstream model evaluation impact? How would you make decisions when improving one metric harms another?Anthropic · Metrics · Hard
- You suspect data quality issues are being introduced at multiple points in the human-data pipeline, but the team lacks visibility into where drop-offs, disagreements, or rework originate. What observability capabilities would you prioritize first, and how would you decide whether that investment should come before new labeling features?Anthropic · Metrics · Hard
- Assume weekly active usage of the platform is strong, but high-stakes workflows still fall back to Slack threads, docs, and spreadsheets. How would you diagnose the biggest adoption bottlenecks, prioritize the next interventions, and prove your changes moved the platform closer to being the company's center of collaboration?Anthropic · Metrics · Hard
- A design-partner customer says adoption of Claude Tag on a newly launched surface spiked at launch and then stalled. How would you diagnose the problem, what metrics and segmentation would you examine, and how would you determine whether the root cause is onboarding, permissions friction, model behavior, or weak product-market fit for that surface?Anthropic · Metrics · Hard
- Assume many new Claude users sign up but never reach a meaningful first-use moment. How would you diagnose where activation is breaking in the onboarding or first-run experience, what first experiment would you launch, and what guardrail metrics would you use to ensure trust and safety are not harmed?Anthropic · Metrics · Hard
More questions from Anthropic
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop