Databricks-GenAI-Assoc Design Applications Practice Question
A GenAI engineer is building a Databricks RAG application that answers questions over a Delta table containing 40 million support tickets. Users report that simple keyword lookups return irrelevant results because the tickets use inconsistent terminology. The engineer needs semantic retrieval that stays synchronized as new tickets stream in every few minutes. Which Databricks component should be used to serve this retrieval layer?
⚠ Common exam trap
The trap here is assuming that a scheduled embedding job plus a plain table is equivalent to a managed vector index, when only a Delta Sync index keeps retrieval current within minutes without manual reindexing.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A Databricks Vector Search index with a Delta Sync index over the tickets table
Semantic retrieval over frequently changing data requires both embedding-based similarity and automatic synchronization with the source table. A Databricks Vector Search Delta Sync index provides exactly this: it computes and maintains embeddings as the Delta table changes and exposes a low-latency endpoint for similarity queries, so terminology variations are handled by vector proximity rather than literal string matching.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A Feature Store table that materializes ticket embeddings refreshed by a nightly job
Why it's wrong here
Feature Store tables are designed to serve machine learning features for training and batch or streaming inference, not to expose an approximate nearest neighbor similarity search API to an application. A nightly refresh also violates the requirement that retrieval stay synchronized within minutes as new tickets stream in, leaving the index stale.
- ✓
A Databricks Vector Search index with a Delta Sync index over the tickets table
Why this is correct
A Delta Sync index continuously tracks the source Delta table and automatically updates embeddings as new tickets arrive, so semantic similarity search stays current without manual reindexing. This directly addresses the inconsistent terminology because retrieval is based on embedding proximity rather than literal keyword matching, and it scales to millions of rows on Databricks-managed infrastructure.
- ✗
A Delta Live Tables pipeline that computes embeddings and writes them to a Parquet directory
Why it's wrong here
A pipeline can compute embeddings, but writing them to Parquet provides no queryable similarity index and no managed endpoint for nearest neighbor search. The application would have to load and scan the data itself, which does not scale to 40 million rows and does not deliver low-latency semantic retrieval. This solves transformation, not serving.
- ✗
A Databricks SQL warehouse running a LIKE query over the ticket text column
Why it's wrong here
A LIKE query performs literal substring matching on the text column, so tickets phrased with different vocabulary will not match the user's wording. It also scans large volumes of text per query and offers no semantic understanding, which is precisely the failure the users reported. It cannot bridge terminology gaps the way embedding similarity can.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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