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Answer: Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.
The question requires accessing 100,000 JPG bird photographs stored in an Azure blob container from an Azure Machine Learning workspace while minimizing data movement. Option D (Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service) is optimal because it allows direct access to the existing blob storage without copying or moving data, aligning with the requirement to minimize data movement. Option E involves copying data to the workspace's default blob datastore, which incurs unnecessary data transfer. Option A (creating Data Lake) and Option B (using Cosmos DB) introduce additional services and data movement. Option C (using TabularDataset) is unsuitable as TabularDataset is for structured data, not image files. The community discussion strongly supports D, with 100% consensus and comments emphasizing it minimizes movement and is correct on recent exams.
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You are creating a multi-class image classification deep learning model for a bird tracking project. You have 100,000 labeled JPG photographs stored in an Azure blob container. You need to access these files from your Azure Machine Learning service workspace for model training while minimizing data movement.
What should you do?
A
Create an Azure Data Lake store and move the bird photographs to the store.
B
Create an Azure Cosmos DB database and attach the Azure Blob containing bird photographs storage to the database.
C
Create and register a dataset by using TabularDataset class that references the Azure blob storage containing bird photographs.
D
Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.
E
Copy the bird photographs to the blob datastore that was created with your Azure Machine Learning service workspace.