Question 15

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?

A. Create a tf.data.Dataset.prefetch transformation.
B. Convert the images to tf.Tensor objects, and then run Dataset.from_tensor_slices().
C. Convert the images to tf.Tensor objects, and then run tf.data.Dataset.from_tensors().
D. Convert the images into TFRecords, store the images in Cloud Storage, and then use the tf.data API to read the images for training.

References

https://cloud.google.com/architecture/ml-on-gcp-best-practices#store-image-video-audio-and-unstructured-data-on-cloud-storage

Question 1

Question 2

Question 3

Question 4

Question 5

Question 6

Question 7

Question 8

Question 9

Question 10

Question 11

Question 12

Question 13

Question 14

Question 15

Question 16

Question 17

Question 18

Question 19

Question 20

Question 21

Question 22

Question 23

Question 24

Question 25

Question 26

Question 27

Question 28

Question 29

Question 30

Question 31

Question 32

Question 33

Question 34

Question 35

Question 36

Question 37

Question 38

Question 39

Question 40

Question 41

Question 42

Question 43

Question 44

Question 45

Question 46

Question 47

Question 48

Question 49

Question 50

Question 51

Question 52

Question 53

Question 54

Question 55

Question 56

Question 57

Question 58

Question 59

Question 60

Question 61

Question 62

Question 63

Question 64

Question 65

Question 66

Question 67

Question 68

Question 69

Question 70

Question 71

Question 72

Question 73

Question 15