Databricks-GenAI-Assoc Application Development Practice Question
Exhibit
{
"endpoint_name": "rag-bot-v1",
"config": {
"served_models": [
{
"model_name": "llama-3-8b",
"model_version": "5",
"workload_size": "Small",
"scale_to_zero_enabled": true
}
]
}
}Refer to the exhibit. A developer is deploying a model using the provided JSON configuration. What is the primary benefit of setting 'scale_to_zero_enabled' to true in this production RAG application?
⚠ Common exam trap
Candidates often confuse 'scale_to_zero_enabled' with model accuracy improvements or latency reduction, forgetting that its primary purpose is exclusively cost optimization by eliminating idle compute charges during inactive periods.
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
✓
It reduces operational costs by shutting down compute resources when the endpoint is not serving traffic.
Setting 'scale_to_zero_enabled' to true allows the Databricks Model Serving endpoint to automatically shut down compute resources when no requests are being processed. This is highly beneficial for cost optimization, as it eliminates idle runtime costs during periods of inactivity. When a new request arrives, the service automatically initializes the model, ensuring cost efficiency without requiring manual intervention to scale the underlying infrastructure up or down.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It increases the throughput of the model by optimizing memory allocation during inference.
Why it's wrong here
Scaling to zero has no impact on memory allocation or model throughput. It is strictly an infrastructure management configuration designed to reduce costs by powering down resources during idle periods. Performance optimization is typically handled by adjusting the workload size or configuring model-specific hardware requirements instead.
- ✗
It ensures that the model always stays warm to minimize latency for every user request.
Why it's wrong here
Scaling to zero actually does the opposite of keeping a model warm. By powering down resources when idle, the first request after an idle period will experience a 'cold start' latency penalty while the serving infrastructure initializes. This configuration prioritizes cost savings over immediate request-time responsiveness.
- ✓
It reduces operational costs by shutting down compute resources when the endpoint is not serving traffic.
Why this is correct
The primary purpose of enabling scale-to-zero is cost reduction. In environments where request patterns are sporadic or non-continuous, this feature ensures that the company is only billed for the compute resources actually consumed during active inference periods, rather than paying for constant uptime when no requests are present.
- ✗
It enables high availability by automatically replicating the model across multiple availability zones.
Why it's wrong here
Scale-to-zero is an infrastructure lifecycle setting and does not relate to multi-zone replication or high availability configurations. High availability in Databricks Model Serving is achieved through built-in platform features and configuration of multiple replicas, rather than through the power-management logic of scaling to zero during idle periods.
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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.