Databricks-GenAI-Assoc Design Applications Practice Question
In a RAG application, which architectural component ensures that the system handles changes in the source data effectively?
⚠ Common exam trap
Candidates often select manual scheduling tools or general caching mechanisms instead of recognizing Delta Lake Change Data Feed as the native engine tracking incremental table changes.
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 Delta Lake Change Data Feed (CDF).
The Delta Lake Change Data Feed (CDF) or the automatic synchronization of Vector Search indexes is designed specifically to track and apply changes (inserts, updates, deletes) to downstream systems. This ensures that the vector representation of the data is always in sync with the source of truth, avoiding the need for expensive full re-indexes and ensuring that retrieved information is always up-to-date.
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 LLM's internal knowledge base.
Why it's wrong here
The LLM's knowledge is static, based on its training data. It cannot be updated with new source data via a RAG synchronization mechanism. RAG is designed precisely to provide external, dynamic context because the LLM itself cannot adapt to changes in the source data in real-time.
- ✓
The Delta Lake Change Data Feed (CDF).
Why this is correct
CDF provides a structured way to track changes in Delta tables. By leveraging CDF, the vector search index can incrementally ingest only the delta between the old and new states of the data. This is significantly more efficient than full re-indexing and ensures high data consistency with minimal compute cost.
- ✗
The prompt engineering template.
Why it's wrong here
Prompt engineering defines how the model interprets context, but it has no role in managing or synchronizing the underlying data. Changing a prompt template does not update the vector index or reflect changes in the source data; it only alters the way the retrieved data is presented to the LLM.
- ✗
The user session cache.
Why it's wrong here
Caches are designed to speed up retrieval by storing previous results. They do not manage data synchronization or track changes in source data. In fact, caching can be detrimental if the data changes, as it might serve stale information, which is the opposite of the desired behavior for maintaining consistency.
Visual reference
About these practice questions
One of 330 original Databricks-GenAI-Assoc practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.