Databricks-GenAI-Assoc Application Development Practice Question
Exhibit
Refer to the exhibit.
# Configuration snippet for model serving
{
"name": "my-llm-endpoint",
"config": {
"served_models": [{
"model_name": "my-model",
"model_version": "1",
"workload_type": "CPU",
"scale_to_zero_enabled": true
}]
}
}A developer is configuring a model serving endpoint as shown in the exhibit. They observe that the endpoint fails to respond quickly to the first request after a period of inactivity. What is the cause of this behavior?
⚠ Common exam trap
Candidates often mistake this for a network latency issue or an API rate limit. The 'scale-to-zero' configuration is a cost-saving feature that inherently introduces a cold-start delay.
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
✓
The endpoint is entering a cold-start phase due to scale-to-zero.
The 'scale_to_zero_enabled' flag is set to true, which instructs the Databricks infrastructure to shut down the compute resources entirely when no traffic is detected to save costs. When a new request arrives, the infrastructure must perform a 'cold start,' initializing the containers and loading the model into memory. This introduces latency, which is expected behavior when optimizing for infrastructure cost over instant availability in a serverless environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model version is incompatible with the CPU workload type.
Why it's wrong here
CPU workload types are compatible with most standard model formats. If there were an incompatibility, the endpoint would likely fail to deploy entirely rather than exhibiting latency on initial requests, making this explanation inconsistent with the observed behavior of slow response times following a period of inactivity.
- ✓
The endpoint is entering a cold-start phase due to scale-to-zero.
Why this is correct
Setting 'scale_to_zero_enabled' to true triggers the termination of compute resources during idle periods to minimize costs. The subsequent latency is caused by the time required to provision new compute and load the model back into memory, which is the intended mechanism for this serverless configuration setting.
- ✗
The model name is not registered in the Unity Catalog.
Why it's wrong here
If the model name were unregistered, the endpoint deployment would fail during the creation process, and the endpoint would remain in a 'Failed' state. This does not explain why the endpoint provides responses after a delay, as the error would be systemic and prevent any successful inference request.
- ✗
The workload type 'CPU' is incorrectly specified as a string.
Why it's wrong here
The 'workload_type' field requires a string input representing the compute architecture. Providing 'CPU' is a valid configuration value in the API schema. Therefore, the syntax is correct, and the latency issue is strictly related to the resource management behavior defined by the scaling policy, not the syntax.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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-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.