Databricks-GenAI-Assoc Application Development Practice Question
An engineer is troubleshooting a Vector Search index that fails to update. The index relies on a Delta table that is frequently updated. What is the most likely cause for the index failing to reflect new data?
⚠ Common exam trap
Test-takers often assume that updating the source Delta table automatically updates the Vector Search index in real time without needing a synchronization trigger.
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
✓
The synchronization process was not triggered after the table update.
Vector Search indexes are managed assets that require periodic synchronization with the source Delta table. If the synchronization process is not triggered automatically or via an API call, the index will remain stale. Understanding the lifecycle and sync mechanisms of Vector Search is essential for developers to ensure that RAG systems retrieve the most current information, as static indexes quickly lose relevance in dynamic data environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The Delta table must be converted to a Parquet format.
Why it's wrong here
Vector Search natively supports Delta tables as the underlying data source. Converting to Parquet is unnecessary and would actually remove the transactional features, such as change data feed, which are required for efficient synchronization and incremental updates of the vector index, thus making the system less performant and reliable.
- ✓
The synchronization process was not triggered after the table update.
Why this is correct
Vector Search indexes require an explicit trigger or a periodic sync configuration to ingest changes from the source Delta table. Without this action, the index does not automatically update, causing the RAG application to rely on outdated embeddings, which directly impacts the accuracy and relevance of the generative model's output.
- ✗
The embedding model version changed in the middle of a sync.
Why it's wrong here
Vector Search handles the embedding transformation based on the configuration provided during index creation. While changing models would require a re-index, it would not cause an update failure. The system is designed to handle consistent embedding logic, and a mismatch would result in failed lookups rather than update failures.
- ✗
Unity Catalog permissions do not allow read access to the source table.
Why it's wrong here
If the permissions were insufficient, the initial index creation would fail, not the incremental updates. Once an index is established, the service principal or underlying process already has the necessary read access to the source table, making this an unlikely cause for failures specifically occurring during update operations.
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.