Databricks-GenAI-Assoc Application Development Practice Question
Exhibit
Refer to the exhibit.
# Model Serving Policy Configuration
{
"traffic_config": {
"routes": [
{
"served_model_name": "model-v1",
"traffic_percentage": 90
},
{
"served_model_name": "model-v2",
"traffic_percentage": 10
}
]
}
}A developer deploys a new model version as shown in the exhibit. What is the purpose of this configuration?
⚠ Common exam trap
Candidates often misinterpret traffic splitting as a load balancing or scaling technique, rather than identifying its primary purpose: testing new models against live traffic to minimize risk.
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
✓
To conduct A/B testing or a canary rollout.
This configuration implements a canary deployment strategy by splitting traffic between two model versions. By routing 90% of requests to the stable version and 10% to the new version, the team can monitor performance and accuracy in a real-world scenario with minimal risk. This is a standard practice for safely rolling out model improvements, allowing teams to collect data on the new version's behavior before a full-scale transition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To increase compute capacity for the primary model.
Why it's wrong here
Traffic splitting does not manage compute capacity. The number of instances and the resources allocated to each model version are determined by the scaling policy and workload types, not the traffic distribution settings, which simply control the routing of user requests between the available versions.
- ✓
To conduct A/B testing or a canary rollout.
Why this is correct
Configuring traffic percentages allows for controlled, incremental rollouts, which are essential for A/B testing or canary deployments. This allows developers to validate the new model's performance on live traffic while minimizing the impact if the new model version exhibits unexpected behavior or suboptimal accuracy metrics.
- ✗
To load balance between two different cloud regions.
Why it's wrong here
The 'served_model_name' refers to specific versions of a model deployed on the same serving endpoint. Traffic routing within a serving endpoint is local to the service and does not involve multi-region load balancing, which is a separate infrastructure-level configuration that is not managed through this endpoint routing policy.
- ✗
To force all traffic to model-v1 when model-v2 errors.
Why it's wrong here
This configuration represents a static split, not an automatic failover policy. If 'model-v2' returns errors, the endpoint will continue to route 10% of traffic to it. The system does not possess built-in intelligence to detect model-level errors and automatically redirect traffic back to the primary model.
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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.