Databricks-ML-Pro ML Ops Practice Question
A data science team at a retail company has registered a demand forecasting model in the MLflow Model Registry. The model version is currently in the 'Staging' stage and has been validated by the QA team. Before promoting it to 'Production', the ML engineer wants to ensure that the model's performance does not degrade when serving live traffic. They decide to deploy the model to a small percentage of production traffic while continuing to serve the existing model. Which Databricks feature should they use to achieve this?
⚠ Common exam trap
It's easy for candidates to confuse model deployment automation (webhooks, jobs) with traffic management, which requires a serving feature that can split requests between versions.
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 Model Serving with traffic splitting
Databricks Model Serving natively supports traffic splitting, which lets you distribute incoming requests across multiple model versions on the same endpoint. This is ideal for canary deployments: you can send a small fraction of traffic to the new version, monitor metrics, and then increase the percentage. Other options lack the real-time routing capability required to test a model under live production load without affecting all users.
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 Model Serving with traffic splitting
Why this is correct
Databricks Model Serving supports traffic splitting, allowing you to route a percentage of requests to a new model version while the rest go to the existing version. This enables safe canary deployments and A/B testing. By configuring traffic splitting on the endpoint, the team can gradually shift traffic and monitor performance without impacting all users.
- ✗
Databricks Jobs with conditional task execution
Why it's wrong here
Databricks Jobs orchestrate tasks such as data processing or model training, and conditional execution can branch based on task outcomes. However, Jobs are not designed for real-time inference traffic management. They cannot split incoming REST API requests between model versions. Therefore, this approach does not provide the canary deployment capability needed for live traffic testing.
- ✗
MLflow Model Registry webhooks
Why it's wrong here
MLflow Model Registry webhooks trigger automated actions when model version stages change, such as sending notifications or starting CI/CD pipelines. They do not control traffic routing or deployment percentages. While useful for automation, webhooks alone cannot split live traffic between model versions, so they do not meet the requirement of serving a small percentage of production traffic.
- ✗
MLflow Projects with Docker environments
Why it's wrong here
MLflow Projects package code and dependencies for reproducibility, and Docker environments ensure consistent execution. They are used for training or batch inference, not for serving real-time traffic. They do not offer any mechanism to route a percentage of live requests to a new model. Thus, they are irrelevant to the scenario of gradual traffic shifting.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-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-ML-Pro 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-ML-Pro exam.