Google Exam Professional Machine Learning Engineer Topic 6 Question 64 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam

Question #: 64
Topic #: 6

[All Professional Machine Learning Engineer Questions]

You work for a hotel and have a dataset that contains customers' written comments scanned from paper-based customer feedback forms which are stored as PDF files Every form has the same layout. You need to quickly predict an overall satisfaction score from the customer comments on each form. How should you accomplish this task'?

AUse the Vision API to parse the text from each PDF file Use the Natural Language API
analyzesentiment feature to infer overall satisfaction scores.

BUse the Vision API to parse the text from each PDF file Use the Natural Language API
analyzeEntitysentiment feature to infer overall satisfaction scores.

CUptrain a Document Al custom extractor to parse the text in the comments section of each PDF file. Use the Natural Language API analyze sentiment feature to infer overall satisfaction scores.

DUptrain a Document Al custom extractor to parse the text in the comments section of each PDF file. Use the Natural Language API analyzeEntitySentiment feature to infer overall satisfaction scores.

Show Suggested Answer

Suggested Answer: D

Applying quantization to your SavedModel by reducing the floating point precision can help reduce the serving latency by decreasing the amount of memory and computation required to make a prediction. TensorFlow provides tools such as the tf.quantization module that can be used to quantize models and reduce their precision, which can significantly reduce serving latency without a significant decrease in model performance.

by Stevie at Sep 02, 2023, 02:39 AM

Limited Time Offer

25%

Off

Get Premium Professional Machine Learning Engineer Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

King

11 hours ago

I see both points, but I think option D might be the most comprehensive approach. By uptraining a custom extractor and using analyzeEntitySentiment, we can capture both sentiment and entities for a more detailed analysis.

upvoted 0 times

...

Susana

3 days ago

I disagree, I believe option C is more suitable. By uptraining a custom extractor, we can focus on the comments section for better accuracy in predicting satisfaction scores.

upvoted 0 times

...

Hayley

17 days ago

I think option A is the best choice because we can use the Vision API to extract text and then analyze sentiment to predict satisfaction scores.

upvoted 0 times

...