Metrics question
How would you make sure that search results on a search engine are relevant?
- Microsoft
- Metrics
- Medium
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What this question tests
Tests building a metric framework for search relevance, distinguishing objective correctness signals from subjective user satisfaction signals.
How to approach it
- Clarify scope: relevance for a specific query type (navigational, informational, transactional), since the right metric differs.
- Define relevance at the query level using human-rated relevance scores (e.g., NDCG) on a sample of queries as the ground truth.
- Add behavioral proxy metrics: click-through rate on top results, and especially reformulation rate, when a user immediately rephrases the same search, which signals poor relevance.
- Track zero-result and low-click-through-rate queries as a direct list of relevance failures to act on.
- Segment by query type and query popularity (head versus long-tail), since long-tail queries are harder and need separate tracking.
- Define the guardrail: relevance improvements shouldn't come at the cost of result diversity or increased latency.
What a strong answer includes
- Names a real relevance metric, NDCG, from human-rated judgments as the ground truth, not just click-through rate alone.
- Uses reformulation rate as a specific, well-known proxy for poor relevance, since it's more diagnostic than raw clicks.
- Uses illustrative numbers as assumptions: assume 8% of queries are reformulated within 30 seconds, flagging those for review.
- Separates head queries, high volume and easier to optimize, from long-tail queries, which need broader coverage, not just precision on top results.
Common mistakes
- Relying only on click-through rate, which can be gamed by attractive but irrelevant results.
- Ignoring query reformulation as a signal of failed relevance.
- Not segmenting by query type or popularity.
Likely follow-up questions
- How would you build the human relevance rating process at scale?
- How do you handle relevance for ambiguous queries with multiple valid intents?
- What guardrail would you add against relevance changes that hurt latency?
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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