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Question 163 - Professional Machine Learning Engineer discussion

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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?

A.
1 Upload the audio files to Cloud Storage 2. Call the speech: Iongrunningrecognize API endpoint to generate transcriptions 3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions
Answers
A.
1 Upload the audio files to Cloud Storage 2. Call the speech: Iongrunningrecognize 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: Iongrunningrecognize API endpoint to generate transcriptions. 3 Create a Cloud Function that calls the Natural Language API by using the analyzesentiment method
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B.
1 Upload the audio files to Cloud Storage 2 Call the speech: Iongrunningrecognize 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 Tiles 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
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C.
1 Iterate over your local Tiles 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: lengrunningrecognize API endpoint to generate transcriptions 4 Call the Natural Language API by using the analyzesenriment method
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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: lengrunningrecognize API endpoint to generate transcriptions 4 Call the Natural Language API by using the analyzesenriment method
Suggested answer: B

Explanation:

According to the official exam guide1, one of the skills assessed in the exam is to ''design, build, and productionalize ML models to solve business challenges using Google Cloud technologies''.The Speech-to-Text API2allows you to convert audio to text by applying powerful neural network models.The Natural Language API3enables you to analyze text and extract information about the sentiment, entities, and syntax.The Cloud Functions4service lets you write and deploy code that runs in response to events, such as a Pub/Sub message or an HTTP request. Therefore, option B is the most efficient approach to analyze the audio files for customer sentiment, as it leverages the existing Google Cloud services and avoids unnecessary data processing and model training. The other options are not relevant or optimal for this scenario.Reference:

Professional ML Engineer Exam Guide

Speech-to-Text API

Natural Language API

Cloud Functions

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

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asked 18/09/2024
Archana Pingily
34 questions
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