Databricks-ML-Assoc Databricks Machine Learning Practice Question
A machine learning engineer is deploying a model using MLflow Model Serving on Databricks. They want to ensure the endpoint can handle bursts of traffic and automatically scale. Which configuration should they set when creating the served model?
⚠ Common exam trap
Many exam-takers confuse the workload_size parameter, which controls per-replica resources, with the replica count parameters that enable horizontal scaling.
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
✓
Configure the 'min_replicas' and 'max_replicas' parameters to define the scaling range for the endpoint.
To handle traffic bursts with automatic scaling, you must configure the min_replicas and max_replicas parameters. These define the range within which the serving infrastructure can dynamically add or remove replicas based on incoming request load, ensuring the endpoint scales out during peaks and scales in during lulls.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable 'autoscaling' by setting the 'autoscale' flag to true in the served model configuration.
Why it's wrong here
There is no 'autoscale' flag in the MLflow Model Serving configuration. Autoscaling is controlled through min_replicas and max_replicas. Setting a non-existent flag would either be ignored or cause an error, and it would not enable dynamic scaling.
- ✗
Set the 'scale_to_zero_enabled' parameter to true to allow the endpoint to scale down to zero when idle.
Why it's wrong here
While scale_to_zero_enabled allows the endpoint to scale down to zero replicas when idle to save cost, it does not enable automatic scaling for bursts of traffic. It only controls whether the endpoint can shut down completely, which may introduce cold-start latency. It does not provide the dynamic scaling needed for traffic bursts.
- ✗
Set the 'workload_size' parameter to 'Large' to handle high traffic volumes.
Why it's wrong here
The 'workload_size' parameter determines the compute resources (CPU, memory) allocated per replica, not the number of replicas. While a larger workload size can improve per-replica performance, it does not provide horizontal scaling to handle bursts. Scaling out requires adjusting the replica count limits.
- ✓
Configure the 'min_replicas' and 'max_replicas' parameters to define the scaling range for the endpoint.
Why this is correct
MLflow Model Serving on Databricks uses the min_replicas and max_replicas parameters to control automatic scaling. Setting a minimum ensures baseline capacity, while the maximum allows the endpoint to scale out during traffic bursts. This configuration enables the serving infrastructure to dynamically adjust replicas based on load.
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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-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-ML-Assoc exam.