Google Cloud Professional Machine Learning Engineer — Question 195

Your company stores 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 need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API You want to use the most efficient approach. What should you do?

Answer options

• A. 1. Upload the audio files to Cloud Storage 2. Call the speech:longrunningrecognize API endpoint to generate transcriptions 3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions.
• B. 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
• C. 1. Iterate over your local files in Python 2. Use the Speech-to-Text Python library to create a speech.RecognitionAudio object, and set the content to the audio file data 3. Call the speech:recognize API endpoint to generate transcriptions 4. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions.
• D. 1. Iterate over your local files in Python 2. Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object and set the content to the audio file data 3. Call the speech:longrunningrecognize API endpoint to generate transcriptions. 4. Call the Natural Language API by using the analyzeSentiment method

Correct answer: B

Explanation

Option B is correct as it efficiently utilizes Cloud Storage for uploading audio files and leverages the speech:longrunningrecognize API for transcription, followed by analyzing sentiment with the Natural Language API. Other options either do not utilize Cloud Storage or involve unnecessary local processing steps, making them less efficient for handling large audio files.