Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
Exhibit
{
"model_name": "customer_churn",
"model_version": "2",
"endpoint_name": "churn_inference",
"traffic_config": {
"routes": [
{
"served_model_name": "churn_v1",
"traffic_percentage": 90
},
{
"served_model_name": "churn_v2",
"traffic_percentage": 10
}
]
}
}Refer to the exhibit. An engineer is configuring a canary deployment for a churn prediction model. Based on the provided traffic configuration, what is the expected behavior of the endpoint?
⚠ Common exam trap
Candidates often misread the traffic percentages or assume the configuration implies a different strategy, failing to parse the standard canary deployment logic defined in the endpoint traffic policy.
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
✓
90% of requests are routed to churn_v1 and 10% to churn_v2.
The configuration implements a traffic-splitting strategy, directing 90% of requests to 'churn_v1' and 10% to 'churn_v2'. This is a standard pattern for A/B testing or canary releases, allowing teams to validate new models in production with minimal risk. By monitoring the performance of the 10% traffic slice, engineers can decide whether to promote the model, ensuring stability before a full rollout.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The endpoint will error because total traffic must equal 100% for each model version individually.
Why it's wrong here
The configuration is valid as long as the total traffic distribution across all routes sums to 100%. In this case, 90% plus 10% equals 100%, which is a valid traffic policy. The system will correctly route requests according to these defined weights without errors.
- ✓
90% of requests are routed to churn_v1 and 10% to churn_v2.
Why this is correct
The traffic_config explicitly defines the routing weights for the served models. This split enables a canary release where the new version (v2) receives a small fraction of real-world traffic. This allows for performance benchmarking against the baseline (v1) before committing to a full deployment transition.
- ✗
The endpoint will only route traffic to churn_v1 after churn_v2 reaches capacity.
Why it's wrong here
Traffic splitting in Databricks Model Serving is weight-based, not capacity-based. The system will continue to distribute requests according to the defined percentages regardless of the underlying load, provided the instances are healthy. It does not act as a failover mechanism based on capacity thresholds.
- ✗
The endpoint will round-robin requests between v1 and v2 regardless of the weights.
Why it's wrong here
Weights define the probability distribution for incoming requests. A round-robin approach would imply a 50/50 split, which contradicts the explicit 90/10 configuration provided in the exhibit. The routing engine respects the percentages defined in the traffic_config to ensure the desired distribution is maintained over time.
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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.