Answer-first summary for fast verification
Answer: 1. Upload the audio files to Cloud Storage. 2. Call the speech:longrunningrecognize API endpoint to generate transcriptions 3. Create a Cloud Function that calls the Natural Language API by using the analyzeSentiment method
B is the correct answer. Uploading the audio files to Cloud Storage ensures scalable and reliable storage for large datasets. The speech:longrunningrecognize API endpoint is designed for processing longer audio files asynchronously, which improves overall throughput. Using a Cloud Function with the Natural Language API's analyzeSentiment method allows for efficient, serverless processing and sentiment analysis of the transcriptions. This approach leverages managed services that automatically scale, reducing the need for manual infrastructure management.
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Your company has stored a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You have been tasked with analyzing these audio files to determine customer sentiment. To achieve this, you plan to use Google's Speech-to-Text API to convert audio to text and then use a sentiment analysis tool to evaluate the transcriptions for customer sentiment. You need to choose the most efficient and scalable approach for this task. What should you do?
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