Exam Professional Machine Learning Engineer topic 1 question 200 discussion

Document AI custom extractor: Since the layout of the feedback forms is consistent, training a custom extractor in Document AI allows for efficient and accurate extraction of the specific comments section. This ensures the Natural Language API receives the relevant text for sentiment analysis. Natural Language API - analyzeSentiment: This functionality within the Natural Language API is specifically designed to analyze sentiment in a piece of text. It provides an overall sentiment score that can be mapped to a satisfaction score (e.g., high positive sentiment translates to high satisfaction).

Document AI best fit for this use case.

"quickly" is the differentiator between A & C

DocumentAI is perfect for the case. Since the question says: "overall satisfaction", then entity is not needed.

In summary, option A offers the optimal balance of speed, accuracy, and simplicity for this specific task. Using the pre-trained APIs is faster and requires less expertise than training a custom model. The analyzeSentiment function directly addresses the need for an overall satisfaction score. Why not D? If speed is the absolute priority and the layout is truly consistent, the Vision API's speed might outweigh the potential for slightly improved accuracy from a custom extractor.

My vote is a. It is simple and do the job.

C is right Document AI custom extractor: Allows you to train a custom model to extract relevant information (in this case, customer comments) from the PDF files. Natural Language API analyzeSentiment feature: Analyzes the sentiment of the extracted text to predict an overall satisfaction score.

C & D are overkill We don't care about entities sentiment -> B is out Left with A and https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/analyzeSentiment

quickly predict an overall satisfaction -> a

I go with A, because "you need quickly predict", no time for fine-tunning.

My answer: Letter C Document AI is a suitable tool for cases where there are patterns of forms or documentation. Additionally, it is possible to directly read PDF files. In the Natural Language API, the analyzeSentiment function can determine the overall sentiment, as the text asks, "You need to quickly predict an overall satisfaction." The analyzeEntitySentiment function provides a score for each entity or word found. https://cloud.google.com/natural-language/docs/basics

Document AI custom extractor: This allows you to tailor the text extraction specifically to the layout and format of your customer feedback forms, ensuring accurate capture of the comments section. Natural Language API analyzeSentiment: This feature analyzes the extracted text and provides an overall sentiment score, which can be used to gauge customer satisfaction.

Precision in text extraction: Document AI is specifically designed for extracting text from structured documents like forms, ensuring accurate extraction of comments, even with varying handwriting styles. Custom model for form layout: Training a custom extractor tailored to the hotel's feedback form layout further enhances accuracy and targets the relevant comments section effectively. Sentiment analysis: Natural Language API's analyzeSentiment feature analyzes overall sentiment in a text block, aligning with the goal of deriving overall satisfaction scores.