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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'?

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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

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Dorinda
1 months ago
Hmm, I don't know. Option A seems like the simplest approach, and sometimes simple is best, you know? Plus, I heard the Vision API is getting pretty good at handling scanned PDFs these days.
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Willetta
9 days ago
Yeah, I agree. Option A does seem like the most straightforward choice.
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Santos
22 days ago
A) Use the Vision API to parse the text from each PDF file Use the Natural Language API analyzesentiment feature to infer overall satisfaction scores.
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Tresa
23 days ago
Let's go with Option A then. It seems like the most efficient choice.
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Peggie
27 days ago
I agree, simplicity is key. The Vision API has been improving a lot lately.
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Janessa
28 days ago
Option A) Use the Vision API to parse the text from each PDF file Use the Natural Language API analyzesentiment feature to infer overall satisfaction scores.
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Rolande
1 months ago
Haha, I'm feeling a bit sentimental about this question. Option D all the way, baby! Gotta get that entity-level sentiment analysis going on. It's like a fine-tuned sentiment smoothie for my hotel's feedback data.
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Brandee
1 months ago
I'm gonna go with option B. The analyzeEntitySentiment feature could give us some more granular insights into the customer's sentiments, right? That could be really useful for this kind of task.
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Rodney
1 months ago
The correct answer is C. Using the Vision API to extract the text from the PDF files and then leveraging the Natural Language API's analyzeSentiment feature is the way to go. Seems straightforward and efficient.
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King
2 months 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.
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Susana
2 months 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.
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Hayley
2 months 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.
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