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…

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()…

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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