Databricks-GenAI-Assoc Design Applications Practice Question
A GenAI team at a retail company has built a RAG chatbot on Databricks that answers customer questions from a product catalog stored in Delta Lake. The catalog is updated nightly, and the team wants the chatbot to reflect those updates without manual intervention. They are deciding how to keep the Vector Search index synchronized. Which approach should they use?
⚠ Common exam trap
The trap here is assuming that enabling Change Data Feed on the source table is sufficient to refresh a Vector Search index, when the index itself must be configured for Delta Sync.
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
✓
Configure a Vector Search Delta Sync index on the catalog table so the index is refreshed automatically when the source table changes.
A Delta Sync index is the Databricks Vector Search construct that keeps an index aligned with a Delta table, including incremental changes. Because the catalog is a Delta table that changes nightly, configuring Delta Sync lets the chatbot retrieve current product information without a custom rebuild job. The other choices either fail to update the index or introduce unnecessary full recomputation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a Databricks SQL scheduled query to copy the catalog table into a Parquet file and point the retriever at that file each night.
Why it's wrong here
Copying to Parquet does not update the Vector Search index that the chatbot queries. The retriever still reads the existing index, so the chatbot would not reflect new catalog entries. This approach also introduces a second storage format and pipeline that must be maintained, increasing complexity without solving index freshness.
- ✓
Configure a Vector Search Delta Sync index on the catalog table so the index is refreshed automatically when the source table changes.
Why this is correct
A Delta Sync index in Databricks Vector Search tracks a Delta table as its source and can automatically sync new or changed rows, so nightly catalog updates flow into the index without a separate pipeline. This matches the requirement for unattended freshness and keeps retrieval aligned with the Delta Lake table.
- ✗
Create a standard Vector Search index and schedule a daily notebook that deletes the index and recreates it from the catalog table.
Why it's wrong here
Recreating a standard index nightly is disruptive and unnecessary. It forces a full recomputation of embeddings, consumes extra compute, and can leave the endpoint serving stale or missing results during rebuild. Delta Sync indexes are designed to handle incremental updates from a Delta source, so this workaround adds cost and operational risk without benefit.
- ✗
Enable Change Data Feed on the catalog table and rely on it to update the Vector Search index automatically.
Why it's wrong here
Change Data Feed records row-level changes in a Delta table for downstream consumers, but it does not by itself push those changes into a Vector Search index. The index must still be configured as a Delta Sync index or updated by an explicit job. Enabling CDF alone leaves the index unsynchronized with the catalog.
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JA
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
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-GenAI-Assoc exam.