Databricks Certified Context Engineer Associate Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose A0

  - Correct answer : A — Databricks AI Search vector similarity search over embedded memory entries A . Correct. Finding conceptually related content that shares no exact keywords requires comparing embeddings for semantic closeness, which is exactly what Databricks AI Search's vector similarity search provides. B . Incorrect. An exact-match category filter only returns rows that share the same discrete label; it cannot surface conceptually related memories that fall outside that category or use different wording. C . Incorrect. Sorting by recency retrieves memories based on time, not on conceptual relevance to the new ticket's content. D . Incorrect. A join against a fixed lookup table resolves known relationships between records; it does not measure semantic similarity between free-text memory content and a new query.