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Question 218 - Professional Machine Learning Engineer discussion

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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.
Answers
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.
Answers
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.
Answers
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.
Answers
D.
Store the URLs of the images in a CSV file Read the file by using the tf.data.experomental.CsvDataset function.
Suggested answer: C

Explanation:

TFRecords are a binary file format that can store large amounts of data efficiently. By converting the images to TFRecords and storing them in a Cloud Storage bucket, you can reduce the data size and improve the data transfer speed. You can then read the TFRecords by using the tf.data.TFRecordDataset function, which creates a dataset of tensors from the TFRecord files. This way, you can read images at scale during training while minimizing data I/O bottlenecks.Reference:

TFRecord documentation

tf.data.TFRecordDataset documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

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asked 18/09/2024
Cheah Eng Soon
34 questions
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