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

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Dorinda

2 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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Colby

4 days ago

I see your point, but I think sticking with Option A might be more efficient.

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Margo

8 days ago

C) Uptrain a Document AI 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.

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Willetta

26 days ago

Yeah, I agree. Option A does seem like the most straightforward choice.

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Santos

1 months 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

1 months ago

Let's go with Option A then. It seems like the most efficient choice.

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Peggie

1 months ago

I agree, simplicity is key. The Vision API has been improving a lot lately.

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Janessa

2 months 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

2 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

2 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

2 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

3 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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