You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How s

The correct answer is D. Convert the images into TFRecords, store the images in Cloud Storage, and then use the tf.data. Cite from Google Pag: to construct a Dataset from data in memory, use tf.data.Dataset.from_tensors() or tf.data.Dataset.from_tensor_slices(). When input data is stored in a file (not in memory), the recommended TFRecord format, you can use tf.data.TFRecordDataset(). tf.data.Datas

Submitted by mateo_ar· Apr 18, 2026Data processing and feature engineering

Question

You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?

Options

ACreate a tf.data.Dataset.prefetch transformation.
BConvert the images to tf.Tensor objects, and then run Dataset.from_tensor_slices().
CConvert the images to tf.Tensor objects, and then run tf.data.Dataset.from_tensors().
DConvert the images into TFRecords, store the images in Cloud Storage, and then use the tf.data

How the community answered

(39 responses)

A
5% (2)
B
3% (1)
C
13% (5)
D
79% (31)

Explanation

Cite from Google Pag: to construct a Dataset from data in memory, use tf.data.Dataset.from_tensors() or tf.data.Dataset.from_tensor_slices(). When input data is stored in a file (not in memory), the recommended TFRecord format, you can use tf.data.TFRecordDataset(). tf.data.Dataset is for data in memory. tf.data.TFRecordDataset is for data in non-memory storage.

Topics

#tf.data API#TFRecords#Input pipeline#Large-scale data

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