Databricks-ML-Pro Model Deployment Practice Question
Exhibit
{
"served_entities": [
{
"name": "churn-model-v1",
"entity_name": "prod.ml_models.churn_prediction",
"entity_version": "1",
"workload_size": "Small",
"scale_to_zero_enabled": true,
"traffic_config": {
"percent": 90
}
},
{
"name": "churn-model-v2",
"entity_name": "prod.ml_models.churn_prediction",
"entity_version": "2",
"workload_size": "Small",
"scale_to_zero_enabled": true,
"traffic_config": {
"percent": 10
}
}
]
}Refer to the exhibit. A machine learning team has updated their model serving endpoint configuration as shown in the JSON. Which deployment strategy is being implemented, and what is the primary risk associated with this specific configuration?
⚠ Common exam trap
Candidates often overlook the 'cold start' penalty in Canary deployments. They assume that if it works for 10% of traffic, it works for everything, forgetting the resource initialization latency.
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
✓
Canary deployment; the primary risk is increased 'cold start' latency for the v2 model due to low traffic volume.
The exhibit demonstrates a Canary deployment where a small fraction of traffic (10%) is routed to a new model version (v2) while the majority remains on the stable version (v1). This allows for real-world testing with minimal impact. However, since both versions are set to scale-to-zero, the 10% traffic might not be frequent enough to keep v2 warm, leading to high latency for those 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.
- ✗
Blue/Green deployment; the primary risk is the high cost of running two identical clusters simultaneously.
Why it's wrong here
Blue/Green deployment typically involves a full switch from one environment to another rather than a percentage-based split. Furthermore, the use of scale-to-zero in the config contradicts the risk of high idle costs, as the infrastructure will shut down when not actively processing the split traffic.
- ✓
Canary deployment; the primary risk is increased 'cold start' latency for the v2 model due to low traffic volume.
Why this is correct
In a Canary setup with a 90/10 split and scale-to-zero enabled, the v2 instance will likely idle frequently. When the 10% of requests do arrive, the system must provision the instance from scratch, causing significant delays for those specific users compared to the more frequently used v1 version.
- ✗
A/B testing; the primary risk is that the workload size 'Small' is insufficient for 90% of the traffic.
Why it's wrong here
While this could be used for A/B testing, the workload size is a standard setting that can often handle substantial traffic depending on the model complexity. The more immediate and technical risk in this specific JSON configuration is the interaction between the low traffic percentage and the scale-to-zero functionality.
- ✗
Shadow deployment; the primary risk is that v2 will interfere with the predictions returned by v1.
Why it's wrong here
In a shadow deployment, the new model receives traffic but its results are not returned to the end user. The config shown explicitly routes 10% of actual traffic to v2, meaning those users will receive v2's predictions, which confirms this is an active Canary deployment rather than a Shadow one.
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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.