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Answer: Run the real-time inference pipeline.
The correct answer is A: Run the real-time inference pipeline. According to Azure Machine Learning documentation and best practices, you must run the real-time inference pipeline at least once before deployment to validate the pipeline, ensure all components work correctly, and create the necessary artifacts in the experiment history. This is explicitly stated in Microsoft's official tutorials and supported by multiple community comments with upvotes. Option D (Create an Azure Machine Learning compute cluster) is incorrect because the question states you've already created and run a training pipeline, implying you already have compute resources available. Options B and C are irrelevant to deploying a real-time inference pipeline.
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You use Azure Machine Learning designer to create and run a training pipeline. You then create a real-time inference pipeline that must be deployed as a web service.
What must you do before you deploy the real-time inference pipeline?
A
Run the real-time inference pipeline.
B
Create a batch inference pipeline.
C
Clone the training pipeline.
D
Create an Azure Machine Learning compute cluster.
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