Databricks-GenAI-Assoc Application Development Practice Question
A GenAI engineer is building a retrieval-augmented generation application on Databricks. They want to store document embeddings and perform fast approximate nearest-neighbor search without managing a separate vector database. They have already created a source Delta table with columns: id (string), text (string), and embedding (array<float>). Which Databricks feature should they use to create a Vector Search index that automatically syncs with the Delta table?
⚠ Common exam trap
The trap here is assuming that any Databricks storage feature like Delta tables or Feature Store can serve as a vector index, but only Vector Search provides the required similarity search and automatic synchronization.
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
✓
Databricks Vector Search with a Delta Sync index
Databricks Vector Search is the managed service designed for storing embeddings and performing fast similarity search. A Delta Sync index automatically syncs with a source Delta table, so when new documents or embeddings are added, the index updates without manual intervention. This eliminates the need to manage a separate vector database and integrates natively with Databricks workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks Feature Store with a training set
Why it's wrong here
Feature Store is designed to manage feature tables for model training and serving, but it does not provide vector similarity search or an index for embeddings. It can store embeddings as features, but querying nearest neighbors would require custom code and would not offer the automatic sync and serverless scaling of Vector Search.
- ✗
MLflow Model Registry with a custom PyFunc model
Why it's wrong here
MLflow Model Registry is for versioning and deploying machine learning models, not for storing or querying vector embeddings. While you could wrap a retrieval function in a PyFunc model, it does not provide a managed vector index or automatic synchronization with a Delta table. It lacks the low-latency approximate nearest neighbor search required for RAG.
- ✓
Databricks Vector Search with a Delta Sync index
Why this is correct
Databricks Vector Search natively supports Delta Sync indexes that automatically keep embeddings in sync with a source Delta table. You can create an index using the embedding column and specify the source table; Databricks manages the underlying vector database and synchronization. This is the intended serverless vector search capability for RAG applications on Databricks.
- ✗
Delta Live Tables with a materialized view
Why it's wrong here
Delta Live Tables (DLT) is a framework for building reliable data pipelines, and materialized views can precompute query results. However, DLT does not provide vector indexing or approximate nearest neighbor search capabilities. It cannot serve as a vector store for RAG; you would still need a separate vector search solution.
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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
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.