Databricks-ML-Assoc Model Deployment Practice Question
A data scientist needs to deploy a model to Databricks Model Serving. Which component is strictly required to be logged in MLflow to enable the 'Model Serving' feature?
⚠ Common exam trap
Candidates often believe that logging the model object alone is sufficient for serving. They overlook the signature, which is mandatory for the serving endpoint to validate input schemas.
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 model signature defining input and output schema
Databricks Model Serving requires a model artifact to be logged with a signature. The signature defines the expected input schema, which allows the serving endpoint to validate incoming requests. Without a signature, the model cannot be correctly parsed by the serving infrastructure, preventing the deployment from starting. This ensures that the production inference endpoint operates with expected data formats, maintaining reliability in downstream applications.
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's training accuracy metrics
Why it's wrong here
While tracking metrics is critical for model evaluation and versioning, Databricks Model Serving does not require specific accuracy scores to initiate an endpoint. The infrastructure focuses on the model artifact and signature to facilitate inference calls, rather than performance validation, which is handled at the CI/CD pipeline stage.
- ✓
The model signature defining input and output schema
Why this is correct
The model signature provides the necessary schema metadata for the serving endpoint. This allows Databricks to enforce input validation for all incoming REST API requests. By defining the signature during the log_model call, you ensure the serving container knows how to translate JSON payloads into the correct format.
- ✗
A Unity Catalog registered function
Why it's wrong here
Unity Catalog functions are useful for data transformation but are not a mandatory prerequisite for deploying a basic MLflow model to a serving endpoint. While they can be used in feature pipelines, the serving endpoint specifically requires the model artifact and its associated schema to successfully load the environment.
- ✗
A dedicated high-concurrency cluster
Why it's wrong here
Model Serving endpoints in Databricks are managed services that run on dedicated serverless infrastructure. They do not require a user-managed High Concurrency cluster to function. The serving service handles the compute allocation dynamically based on the traffic configuration, abstracting away cluster management tasks from the deployment process entirely.
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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
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