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Answer: Use AutoML Natural Language to extract custom entities for classification.
The correct answer is B: Use AutoML Natural Language to extract custom entities for classification. This approach leverages Google Cloud’s AutoML to train a custom natural language processing model that can specifically understand and classify the products mentioned in the transcribed calls. This method minimizes data preprocessing and development time, as it automates many aspects of model building and can be tailored to the specific needs of the company's product classification. Options A and D require more development effort and expertise, while option C might not be as effective for custom, company-specific products.
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You work for a large technology company that aims to modernize its contact center to improve efficiency and customer service. The goal is to develop a machine learning solution to classify incoming calls based on the product being mentioned, so that these calls can be routed more quickly to the appropriate support team. The audio of the calls has already been transcribed into text using the Speech-to-Text API. Given the need to minimize data preprocessing and development time, what approach should you take to build the model?
A
Use the AI Platform Training built-in algorithms to create a custom model.
B
Use AutoML Natural Language to extract custom entities for classification.
C
Use the Cloud Natural Language API to extract custom entities for classification.
D
Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm.
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