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You are developing an ML model to identify your company s products in images.

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You are developing an ML model to identify your company s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex Al Training You need to read images at scale during training while minimizing data I/O bottlenecks What should you do?

A.

Load the images directly into the Vertex Al compute nodes by using Cloud Storage FUSE Read the images by using the tf .data.Dataset.from_tensor_slices function.

B.

Create a Vertex Al managed dataset from your image data Access the aip_training_data_uri

environment variable to read the images by using the tf. data. Dataset. Iist_flies function.

C.

Convert the images to TFRecords and store them in a Cloud Storage bucket Read the TFRecords by using the tf. ciata.TFRecordDataset function.

D.

Store the URLs of the images in a CSV file Read the file by using the tf.data.experomental.CsvDataset function.

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