Azure ML Batch Endpoint Deployment Order
You manage an Azure Machine Learning workspace. An MLflow model is already registered. You plan to customize how the deployment does inference. You need to deploy the MLflow model to a batch endpoint for batch inferencing. What should you create first?
Community Votes
67% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The exam tests the logical dependency chain of deployment artifacts; the common trap is confusing the order between creating the definition (metadata) versus the actual execution code (scoring script).
This question tests the prerequisite components for deploying an MLflow model to a batch endpoint in Azure Machine Learning, with community consensus favoring the scoring script as the initial creation step.
Community Discussion (3 comments)
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Expert Analysis
Why the Answer Is Correct
In the context of Azure Machine Learning batch deployments, particularly when using MLflow models, the scoring script is the fundamental artifact that defines how the model performs inference. While the deployment definition acts as a template or configuration object, the scoring script contains the actual Python logic required to load the model and process input data. Without a valid scoring script, the deployment cannot function, making it the primary component to establish first in the workflow.Why the Other Options Are Wrong
A deployment (Option B) cannot be created without referencing both an environment and a scoring script, so it is not the first step. An environment (Option C) is necessary for dependencies but does not contain the inference logic itself. A deployment definition (Option D) is a configuration file or object that references the scoring script and environment; while you might define the structure early, the scoring script is the core executable asset that must exist to make the deployment viable.Community Comment Notes
Community comments show a split opinion, with some arguing for the deployment definition (D) based on infrastructure-as-code workflows, while others (including the suggested answer) emphasize the scoring script (A) as the functional starting point. Comment [1] highlights that initialization requires the run logic found in the scoring script. Comment [2] argues for D, suggesting scripts can be added later, which contradicts the strict dependency where the definition points to the script. The majority vote (67%) supports A, indicating this is the expected 'best practice' answer for this specific exam scenario.Official Reference
Exam Strategy
When asked about deployment prerequisites, prioritize the artifact that contains the actual business logic or inference code (like the scoring script) over the metadata containers (like definitions or environments).