Databricks-GenAI-Assoc Design Applications Practice Question
A team is deploying a Databricks RAG chatbot that must serve interactive traffic with low latency while also allowing the data science team to test prompt variations safely. The engineer wants the production endpoint to keep serving stable traffic even while a new prompt template is being evaluated. Which approach best meets these requirements?
⚠ Common exam trap
The trap here is assuming a single endpoint can host both stable and experimental prompts safely, when shared capacity and shared code paths let an experiment degrade production traffic.
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
✓
Create a separate Model Serving endpoint for the experimental prompt and route a small percentage of traffic to it for comparison
Separating the experimental prompt onto its own Model Serving endpoint preserves production stability while enabling controlled comparison. The team can direct a small share of traffic to the experimental endpoint, measure quality and latency, and promote the prompt only after it proves better, all without redeploying or interrupting the production endpoint.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Lower the production endpoint's concurrency limit and run the experiment on the same endpoint during off-peak hours
Why it's wrong here
Sharing one endpoint couples the experiment's resource consumption to production capacity, so an experimental burst can starve production requests and increase tail latency. Off-peak testing also limits evaluation to unrepresentative traffic and provides no isolation if the experimental prompt misbehaves.
- ✗
Edit the prompt template in the production endpoint's registered model version and restart the endpoint
Why it's wrong here
Editing the production model version and restarting the endpoint forces a redeploy and briefly interrupts serving, and any regression in the new prompt immediately affects all users. It also destroys the ability to compare against the previous prompt, since the prior version is no longer what production runs.
- ✗
Add the new prompt as a branch inside the production chain and select it at runtime using a random number generator
Why it's wrong here
Random runtime selection means some production users receive the untested prompt immediately, so a defect or latency regression reaches real traffic before evaluation completes. It also entangles experimental logic with production code, complicating rollback and making it hard to attribute quality changes to a specific prompt version.
- ✓
Create a separate Model Serving endpoint for the experimental prompt and route a small percentage of traffic to it for comparison
Why this is correct
Isolating the experimental prompt on its own serving endpoint lets the team evaluate it with real or synthetic traffic without risking the production endpoint's stability. Traffic can be split at the application layer for A/B comparison, and if the experiment degrades, production remains unaffected because the two endpoints scale and version independently.
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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.