Databricks-ML-Pro Model Deployment Practice Question
A data science team has deployed a model to Databricks Model Serving and wants to ensure that the endpoint can handle sudden spikes in traffic without manual intervention. Which feature should they configure?
⚠ Common exam trap
Candidates often confuse scale-to-zero with autoscaling; scale-to-zero reduces cost during idle periods but can cause cold starts, while autoscaling adds replicas to handle increased load.
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
✓
Autoscaling with a defined minimum and maximum replica count.
Autoscaling is the Databricks Model Serving feature that dynamically adjusts the number of replicas based on traffic, allowing the endpoint to handle spikes without manual intervention. Configuring minimum and maximum replicas ensures that scaling is bounded and cost-effective. Other options either provide static capacity or are not designed for dynamic load handling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A larger workload type with more memory and CPU.
Why it's wrong here
A larger workload type increases the resources per replica but does not automatically scale the number of replicas. It can handle higher throughput per replica but may still be overwhelmed by sudden spikes. Autoscaling is required to dynamically add replicas in response to traffic, which a static workload type cannot do.
- ✗
Deploy multiple endpoints and use a round-robin DNS.
Why it's wrong here
Managing multiple endpoints and DNS-based load balancing is not a native Databricks feature and adds operational complexity. Databricks Model Serving provides built-in autoscaling within a single endpoint, which is simpler and more efficient. Using multiple endpoints would require external coordination and does not automatically scale based on traffic.
- ✗
Enable scale-to-zero to reduce cold starts during spikes.
Why it's wrong here
Scale-to-zero shuts down replicas when idle, which can actually cause cold starts when traffic resumes. It is designed for cost savings during periods of no traffic, not for handling spikes. In fact, scale-to-zero can increase latency during the first requests after idle, so it is not suitable for ensuring smooth handling of sudden traffic increases.
- ✓
Autoscaling with a defined minimum and maximum replica count.
Why this is correct
Autoscaling automatically adjusts the number of replicas based on incoming traffic, ensuring the endpoint can handle spikes without manual scaling. By setting minimum and maximum replicas, you bound the scaling to control cost and capacity. This is the intended feature for handling variable load in Databricks Model Serving.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.