Databricks-ML-Pro Model Deployment Practice Question
An ML engineer is deploying a model to Databricks Model Serving and needs to ensure that the endpoint can handle traffic spikes while minimizing costs during idle periods. The engineer considers enabling scale-to-zero and configuring autoscaling. Which TWO statements about these features are correct? (Choose two.)
⚠ Common exam trap
The trap here is thinking that scale-to-zero and autoscaling are mutually exclusive or that autoscaling scales on CPU, when it actually scales on request concurrency.
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
✓
Scale-to-zero reduces replicas to zero after a period of inactivity, which can lead to cold-start latency on the next request.
Scale-to-zero reduces replicas to zero during inactivity, causing cold-start latency on the next request. Autoscaling adjusts replicas based on load, up to a maximum. These features can be used together, with scale-to-zero effectively setting the minimum replicas to zero. Autoscaling uses request concurrency as the primary scaling metric, not CPU utilization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Scale-to-zero and autoscaling cannot be enabled simultaneously on the same endpoint.
Why it's wrong here
Scale-to-zero and autoscaling can be enabled together. Scale-to-zero is essentially a special case of autoscaling where the minimum replica count is zero. When both are enabled, the endpoint scales down to zero during idle periods and scales up based on demand, combining cost savings with elasticity.
- ✓
Scale-to-zero reduces replicas to zero after a period of inactivity, which can lead to cold-start latency on the next request.
Why this is correct
Scale-to-zero is designed to save costs by scaling down to zero replicas when there is no traffic. When a request arrives after idle time, the endpoint must scale up, causing a cold start and increased latency for that request. This is a fundamental trade-off between cost and latency.
- ✗
Autoscaling scales based on CPU utilization of the serving containers, not on request concurrency.
Why it's wrong here
Databricks Model Serving autoscaling primarily uses request concurrency (or queue depth) as the metric for scaling decisions, not CPU utilization. This is because inference workloads are often I/O or GPU-bound, and concurrency better reflects the need for additional replicas. Scaling on CPU alone could be inefficient.
- ✗
Scale-to-zero is only available for GPU workload types, not for CPU workload types.
Why it's wrong here
Scale-to-zero is available for both CPU and GPU workload types in Databricks Model Serving. It is a general feature that helps reduce costs when the endpoint is idle. The choice of workload type does not restrict the ability to enable scale-to-zero.
- ✓
Autoscaling automatically adjusts the number of replicas based on incoming request load, up to a maximum configured limit.
Why this is correct
Autoscaling dynamically increases or decreases replicas in response to traffic. It scales out to handle spikes and scales in when load decreases, within the minimum and maximum replica bounds set by the user. This helps maintain performance while controlling costs.
About these practice questions
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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.