Databricks-ML-Pro Model Deployment Practice Question
Exhibit
{
"name": "churn-model-endpoint",
"config": {
"served_models": [
{
"model_name": "churn-model",
"model_version": "5",
"workload_size": "Small",
"scale_to_zero_enabled": true
}
]
}
}Refer to the exhibit. The JSON configuration represents an existing Databricks Model Serving endpoint. You need to update this endpoint to support a traffic split between version 5 and version 6 for A/B testing. Which update strategy is correct?
⚠ Common exam trap
Candidates try to create multiple endpoints for A/B testing rather than modifying the `served_models` array within a single endpoint configuration to split traffic weights.
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
✓
Modify the served_models array to include both versions with specific traffic weights assigned to each.
To perform A/B testing, you must define multiple served models within the `served_models` array in the endpoint configuration. Each entry requires a `traffic` weight that sums to 100%. This allows Databricks to route incoming requests according to the specified percentages. This method is crucial for safely rolling out new models, as it allows you to observe performance metrics on a subset of real-world traffic before a full production cutover.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Update the config to replace 'model_version': '5' with 'model_version': '6' in the current served_models list.
Why it's wrong here
Replacing the version performs an immediate, full cutover rather than a traffic split. This approach provides no way to compare performance between versions or gradually transition users, which is the primary goal of A/B testing. It lacks the safety mechanisms required for validating model performance in production.
- ✗
Create two separate endpoints, one for version 5 and one for version 6, then split traffic using a load balancer.
Why it's wrong here
Managing two distinct endpoints increases complexity, cost, and management overhead. Databricks natively supports traffic splitting within a single endpoint, which simplifies orchestration, monitoring, and request routing. Using an external load balancer is redundant and introduces unnecessary latency and points of failure outside of the integrated Databricks platform.
- ✓
Modify the served_models array to include both versions with specific traffic weights assigned to each.
Why this is correct
Adding both versions to the `served_models` list enables traffic routing control. By specifying the `traffic` field for each, you can define the percentage of requests allocated to each version. This configuration is the standard method for A/B testing in Databricks, providing safe, granular control over model deployment transitions.
- ✗
Delete the current endpoint and recreate it with a new configuration that includes only version 6.
Why it's wrong here
Deleting and recreating the endpoint causes unnecessary downtime for the model. Traffic splitting should be handled via configuration updates on the existing endpoint to maintain service continuity. Recreating the endpoint is inefficient and disrupts the persistent logs and monitoring data gathered for the currently deployed model version.
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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-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.