Metrics question

How would you make sure that search results on a search engine are relevant?

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 building a metric framework for search relevance, distinguishing objective correctness signals from subjective user satisfaction signals.

How to approach it

  1. Clarify scope: relevance for a specific query type (navigational, informational, transactional), since the right metric differs.
  2. Define relevance at the query level using human-rated relevance scores (e.g., NDCG) on a sample of queries as the ground truth.
  3. 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.
  4. Track zero-result and low-click-through-rate queries as a direct list of relevance failures to act on.
  5. Segment by query type and query popularity (head versus long-tail), since long-tail queries are harder and need separate tracking.
  6. Define the guardrail: relevance improvements shouldn't come at the cost of result diversity or increased latency.

What a strong answer includes

Common mistakes

Likely follow-up questions

More metrics questions

More questions from Microsoft

Learn the skill behind it

Chapters of the AI PM course that teach what this question tests.

Preparing for a specific role?

Book summaries for this kind of question

Browse all 4,000+ questions in the bank