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Exam Professional Machine Learning Engineer All Questions

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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 Hide Answer
Suggested Answer: D 🗳️
by [deleted] at June 2, 2021, 10:38 p.m.

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Highly Voted 3 years, 5 months 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 28 times
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maartenalexander
Highly Voted 3 years, 5 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 22 times
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jkkim_jt
Most Recent 1 month 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 2 times
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PhilipKoku
5 months, 2 weeks ago
Selected Answer: D
D) Any rows, 2 columns.
upvoted 1 times
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M25
1 year, 6 months ago
Selected Answer: D
Went with D
upvoted 2 times
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wish0035
1 year, 11 months ago
Selected Answer: D
ans: D
upvoted 1 times
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EFIGO
2 years 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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GCP72
2 years, 3 months ago
Selected Answer: D
Correct answer is "D"
upvoted 1 times
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Mohamed_Mossad
2 years, 5 months ago
Selected Answer: D
will vote for D , as the data shape in instances matches the shape in signature def
upvoted 1 times
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pml2021
2 years, 8 months ago
Selected Answer: D
shape is (-1,2) indicating any no of rows, 2 columns only.
upvoted 2 times
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mousseUwU
3 years, 1 month ago
D is correct if shape(-1,2) means 2 columns for each row
upvoted 3 times
mousseUwU
3 years, 1 month ago
Link to explanation: https://stackoverflow.com/questions/37956197/what-is-the-negative-index-in-shape-arrays-used-for-tensorflow
upvoted 1 times
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Danny2021
3 years, 2 months ago
D: (-1, 2) represents a vector with any number of rows but only 2 columns.
upvoted 5 times
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inder0007
3 years, 5 months ago
Correct answer is D, the shapes otherwise don't matter
upvoted 4 times
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