Google Professional Machine Learning Engineer
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You are designing an input pipeline for a machine learning model that processes high-resolution images from multiple sources at high speed. The dataset is too large to fit into memory, and you need to ensure the pipeline is scalable, cost-effective, and complies with Google's recommended practices for handling large datasets. Which of the following approaches should you implement? (Choose one correct option)
A
Transform the images into tf.Tensor objects and use tf.data.Dataset.from_tensors() to create the dataset, ensuring all data is loaded into memory at once for quick access._
B
Apply the tf.data.Dataset.prefetch transformation to the dataset after loading all images into memory, aiming to improve data loading efficiency by overlapping the preprocessing and model execution.
C
Convert the images into tf.Tensor objects and utilize Dataset.from_tensor_slices() to create the dataset, assuming the slices will manage memory usage effectively.
D
Encode the images into TFRecords format, store them in Cloud Storage, and use the tf.data API to stream the data during training, ensuring efficient data retrieval and minimal memory usage.