Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
You are deploying a RAG application. You need to ensure the model uses the most recent vector data without redeploying the model. What should you use?
⚠ Common exam trap
Many candidates incorrectly think they need to fine-tune or retrain the foundational LLM whenever data changes, missing the efficiency of dynamic RAG architecture.
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
✓
Implement a RAG pattern with a vector database index.
Decoupling the model logic from the data retrieval mechanism is a best practice. By using a retrieval-augmented generation pattern where the retriever fetches data dynamically from a vector store, the model remains static while the knowledge base evolves. This approach is essential for applications requiring real-time information, as it avoids the expensive and slow process of retraining or fine-tuning the base model whenever the underlying data changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the model with new data daily.
Why it's wrong here
Fine-tuning is a resource-intensive process that is not suitable for incorporating dynamic data daily. It risks catastrophic forgetting and does not provide an immediate way to retrieve accurate, up-to-date facts, making it an inefficient solution for RAG applications that need to stay current with rapidly changing information streams.
- ✓
Implement a RAG pattern with a vector database index.
Why this is correct
The RAG pattern retrieves relevant context from a vector database and feeds it into the model's prompt. Since the vector store can be updated independently of the model, this provides a scalable way to ensure the application always has access to the latest data without needing to redeploy models.
- ✗
Re-register the model in MLflow with every update.
Why it's wrong here
Re-registering the model in MLflow is unnecessary and inefficient for updating content. It disrupts the model versioning history and creates unnecessary overhead. The model should remain unchanged while the retrieval component fetches updated context from the vector database, maintaining a clean distinction between the model and the data.
- ✗
Embed all data into the model weights at deployment.
Why it's wrong here
Embedding all data into weights is impossible for large, dynamic datasets and prevents the model from accessing real-time information. This method is highly brittle and inefficient, as any update requires a full model rebuild, which is impractical for production applications that rely on timely and accurate information retrieval.
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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.