Databricks-ML-Assoc Model Deployment Practice Question
A machine learning engineer is preparing to deploy a registered model to a Databricks Model Serving endpoint. Before creating the endpoint, the engineer wants to confirm the deployment prerequisites are satisfied. Which two conditions are required for a successful endpoint creation? (Choose two.)
⚠ Common exam trap
The trap here is treating Model Registry stages as deployment gates, when serving actually keys off the model version URI and logged artifacts.
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 version has a logged MLflow model artifact with a valid model signature
A successful endpoint deployment depends on a loadable MLflow model artifact with a defined signature and a reconstructable dependency environment, because the service must build a container and validate request schemas. Registry stage, attached clusters, and name matching are not technical prerequisites and do not affect whether the endpoint can be created and brought to a ready state.
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 has a logged MLflow model artifact with a valid model signature
Why this is correct
Model Serving requires a logged MLflow model artifact it can load, and a model signature defines the expected input and output schema. Without a signature, the endpoint cannot validate incoming request payloads, so a properly logged model with a signature is a genuine prerequisite for successful endpoint creation.
- ✗
The model version is transitioned to the Production stage in the Model Registry
Why it's wrong here
Model Serving can target a model version by URI regardless of its registry stage. Stage transitions are a governance convenience for organizing the lifecycle, not a technical gate. Requiring Production stage would block valid deployments of versions still in Staging or without any stage assignment, so it is not a prerequisite.
- ✗
The endpoint name matches the registered model name exactly
Why it's wrong here
Endpoint names and registered model names are independent identifiers. An endpoint can serve any registered model version it references, and its name is chosen freely subject to naming rules. Enforcing an exact match would be an artificial constraint that has no bearing on whether endpoint creation succeeds.
- ✗
A dedicated all-purpose cluster is running and attached to the endpoint
Why it's wrong here
Model Serving provisions its own managed compute for the endpoint and does not attach to an all-purpose cluster. Requiring a running cluster reflects a misunderstanding of the serverless serving model. No interactive cluster is needed, and keeping one running would add unnecessary cost without contributing to endpoint creation.
- ✓
The serving environment can be reconstructed from the model's logged dependencies
Why this is correct
The endpoint builds its runtime environment from the conda and requirements files logged with the model run. If those dependencies are missing or cannot be resolved, the container build fails and the endpoint never reaches a ready state, making reconstructable dependencies a required condition for deployment.
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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.