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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 120 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 120
Topic #: 1
[All Professional Machine Learning Engineer Questions]

One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pipeline more robust to issues like this. What should you do?

  • A. Use TensorFlow Data Validation to detect and flag schema anomalies.
  • B. Use TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don’t match the schema with 0.
  • C. Use tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.
  • D. Use custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.
Show Suggested Answer Hide Answer
Suggested Answer: A 🗳️
by mil_spyro at Dec. 13, 2022, 2:07 p.m.

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5a74493
2 months, 3 weeks ago
Selected Answer: A
i would choose A and B because For the model to be truly robust, it needs to adapt to new formats, not just detect and flag anomalies. In this case, combining detection with adaptive preprocessing would be the best approach
upvoted 2 times
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M25
1 year, 6 months ago
Selected Answer: A
Went with A
upvoted 1 times
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Yajnas_arpohc
1 year, 8 months ago
Selected Answer: A
You need to know problem b4 fixing w transform, hence A
upvoted 2 times
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TNT87
1 year, 8 months ago
Selected Answer: A
Answer A
upvoted 1 times
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John_Pongthorn
1 year, 9 months ago
Selected Answer: A
https://www.tensorflow.org/tfx/guide/tfdv#schema_based_example_validation
upvoted 1 times
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ares81
1 year, 10 months ago
Selected Answer: A
Tensorflow Data Validation (TFDV) can analyze training and serving data to: compute descriptive statistics, infer a schema, detect data anomalies. A.
upvoted 1 times
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hiromi
1 year, 11 months ago
Selected Answer: A
A - https://www.tensorflow.org/tfx/data_validation/get_started
upvoted 3 times
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mil_spyro
1 year, 11 months ago
Selected Answer: A
TensorFlow Data Validation (TFDV) is a library that can help you detect and flag anomalies in your dataset, such as changes in the schema or data types. https://www.tensorflow.org/tfx/data_validation/get_started
upvoted 4 times
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A (35%)
C (25%)
B (20%)
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