Exam Professional Machine Learning Engineer topic 1 question 22 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 22
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You trained a text classification model. You have the following SignatureDefs:

You started a TensorFlow-serving component server and tried to send an HTTP request to get a prediction using: headers = {"content-type": "application/json"} json_response = requests.post('http: //localhost:8501/v1/models/text_model:predict', data=data, headers=headers)
What is the correct way to write the predict request?

A. data = json.dumps({ג€signature_nameג€: ג€seving_defaultג€, ג€instancesג€ [['ab', 'bc', 'cd']]})
B. data = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c', 'd', 'e', 'f']]})
C. data = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c'], ['d', 'e', 'f']]})
D. data = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b'], ['c', 'd'], ['e', 'f']]})

Show Suggested Answer

Suggested Answer: D 🗳️

by [deleted] at June 2, 2021, 10:38 p.m.

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[Removed]

Highly Voted 4 years ago

Options: A. data = json.dumps({“signature_name”: “seving_default”, “instances” [[‘ab’, ‘bc’, ‘cd’]]}) B. data = json.dumps({“signature_name”: “serving_default”, “instances” [[‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’]]}) C. data = json.dumps({“signature_name”: “serving_default”, “instances” [[‘a’, ‘b’, ‘c’], [‘d’, ‘e’, ‘f’]]}) D. data = json.dumps({“signature_name”: “serving_default”, “instances” [[‘a’, ‘b’], [‘c’, ‘d’], [‘e’, ‘f’]]})

upvoted 29 times

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maartenalexander

Highly Voted 3 years, 12 months ago

Most likely D. A negative number in the shape enables auto expand (https://stackoverflow.com/questions/37956197/what-is-the-negative-index-in-shape-arrays-used-for-tensorflow). Then the first number -1 out of the shape (-1, 2) speaks the number of 1 dimensional arrays within the tensor (and it can autoexpand) while the second numer (2) sets the number of elements in the inner array at 2. Hence D.

upvoted 23 times

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jkkim_jt

Most Recent 8 months ago

Selected Answer: D

the shape (-1, 2) indicates that the data can have any number of rows (denoted by -1), but must have exactly 2 columns. In machine learning, especially in frameworks like TensorFlow or Keras, the -1 acts as a placeholder for dynamic batch sizes, meaning the model can process inputs with any number of samples (rows), but each sample must have exactly 2 features (columns).

upvoted 4 times

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PhilipKoku

1 year ago

Selected Answer: D

D) Any rows, 2 columns.

upvoted 1 times

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M25

2 years, 1 month ago

Selected Answer: D

Went with D

upvoted 2 times

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wish0035

2 years, 6 months ago

Selected Answer: D

ans: D

upvoted 1 times

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EFIGO

2 years, 6 months ago

Selected Answer: D

Having "shape=[-1,2]", the input can have as many rows as we want, but each row needs to be of 2 elements. The only option satisfying this requirement is D.

upvoted 1 times

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