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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 302 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 302
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You are building an application that extracts information from invoices and receipts. You want to implement this application with minimal custom code and training. What should you do?

  • A. Use the Cloud Vision API with TEXT_DETECTION type to extract text from the invoices and receipts, and use a pre-built natural language processing (NLP) model to parse the extracted text.
  • B. Use the Cloud Document AI API to extract information from the invoices and receipts.
  • C. Use Vertex AI Agent Builder with the pre-built Layout Parser model to extract information from the invoices and receipts.
  • D. Train an AutoML Natural Language model to classify and extract information from the invoices and receipts.
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Suggested Answer: B 🗳️

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Tara3
1 month, 2 weeks ago
Selected Answer: B
My Answer is B. Use the Cloud Document AI API to extract information from the invoices and receipts.  Cloud Document AI service is specifically designed for structured document processing, including invoice and receipt extraction. It uses pre-trained models to identify key fields like dates, amounts, items, and vendor information, minimizing the need for custom training or development.
upvoted 1 times
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hit_cloudie
2 months, 4 weeks ago
Selected Answer: B
B is correct This API is purpose-built for structured document processing (e.g., invoices, receipts). It extracts structured fields like total, date, vendor, etc. Requires minimal custom code and no training Ideal for this use case
upvoted 1 times
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5091a99
5 months, 2 weeks ago
Selected Answer: B
Answer: B. DocumentAI API is much better at extracting specific fields and returning more repeatable results for specific field extraction than Cloud Vision (which is more directed at Object detection, etc.)
upvoted 2 times
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