Databricks-ML-Assoc Model Deployment Practice Question
A machine learning engineer is configuring a Databricks Model Serving endpoint for a model that requires GPU acceleration. They set the workload size to 'GPU_Medium' but the endpoint fails to deploy. Which of the following is the most likely cause of the failure?
⚠ Common exam trap
The trap here is focusing on model artifacts like signature or flavor, when the issue is actually about the underlying compute infrastructure and quota for GPU instances.
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 Databricks workspace does not have GPU instances enabled or available.
GPU workload sizes for Model Serving require the workspace to have GPU instances enabled and available. If the workspace lacks GPU capacity or the user lacks permissions, deployment fails. Other factors like flavor or signature are unrelated to GPU provisioning. Ensuring GPU availability is a prerequisite for GPU-accelerated endpoints.
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 was not logged with the 'mlflow.pyfunc' flavor.
Why it's wrong here
While pyfunc is a common flavor for custom models, GPU support is not dependent on it. Many flavors, such as TensorFlow or PyTorch, can be served with GPU. The failure is more likely due to workspace configuration or quota limits, not the flavor. Thus, this is not the primary cause.
- ✗
The model's signature does not include GPU-specific metadata.
Why it's wrong here
Model signatures do not contain GPU-specific metadata; they only define input and output schemas. GPU requirements are specified at deployment time via workload size, not in the signature. Therefore, a missing GPU metadata in the signature would not cause deployment failure.
- ✗
The endpoint was configured with 'min_instances' set to 0.
Why it's wrong here
Setting min_instances to 0 allows the endpoint to scale to zero, but it does not prevent deployment. The endpoint can still deploy and scale up on demand. While it may affect latency, it is not a cause of deployment failure for GPU workload sizes.
- ✓
The Databricks workspace does not have GPU instances enabled or available.
Why this is correct
Model Serving GPU workload sizes require that the workspace has GPU instances enabled and sufficient quota. If GPU instances are not available or the quota is exceeded, the endpoint deployment will fail. This is a common configuration issue when attempting to use GPU-accelerated serving.
About these practice questions
One of 319 original Databricks-ML-Assoc practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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-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.