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Google Professional Machine Learning Engineer Exam - Topic 5 Question 86 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 86
Topic #: 5
[All Professional Machine Learning Engineer Questions]

You work at an organization that maintains a cloud-based communication platform that integrates conventional chat, voice, and video conferencing into one platform. The audio recordings are stored in Cloud Storage. All recordings have an 8 kHz sample rate and are more than one minute long. You need to implement a new feature in the platform that will automatically transcribe voice call recordings into a text for future applications, such as call summarization and sentiment analysis. How should you implement the voice call transcription feature following Google-recommended best practices?

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Suggested Answer: D

by Raylene at Sep 12, 2024, 01:17 PM

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Tenesha
6 months ago
C could work, but why complicate it with upsampling?
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Xuan
6 months ago
Definitely going with B, it aligns with best practices!
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Hailey
6 months ago
Surprised that upsampling is even suggested, isn't 8 kHz enough?
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Jacqueline
7 months ago
I disagree, A seems more straightforward for real-time needs.
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Katina
7 months ago
I think option B is the best choice for async processing.
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Paul
7 months ago
I vaguely remember that asynchronous recognition might be more suitable for longer recordings, but I’m not entirely sure if upsampling is required.
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Gianna
7 months ago
I feel like using the original sampling rate is safer, but I’m not confident about whether synchronous or asynchronous recognition would yield better results.
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Isadora
7 months ago
I think we practiced a similar question where upsampling was mentioned, but I can't recall if it was necessary for the Speech-to-Text API.
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Lennie
8 months ago
I remember we discussed the importance of using the correct sampling rate for transcription, but I'm not sure if synchronous or asynchronous is better for this case.
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Donette
8 months ago
Upsampling the audio to 16 kHz could be a good idea to improve the transcription accuracy, but I'll need to weigh the trade-offs between that and using the original 8 kHz sample rate.
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Melynda
8 months ago
Hmm, I'm a bit unsure about the differences between synchronous and asynchronous recognition. I'll need to review those concepts before deciding on the best approach.
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Xochitl
8 months ago
This seems like a straightforward question about implementing a voice call transcription feature. I'll need to carefully consider the recommended best practices from Google.
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Lorrine
8 months ago
I think the key is to follow the Google-recommended best practices, so I'll likely go with either option B or D that uses the Speech-to-Text API, since those are the ones explicitly mentioned.
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Justa
8 months ago
I'm not entirely sure, but I feel like improving the look and feel is important too. Maybe B is a contender?
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Galen
2 years ago
I see your point, Alba. Let's go with the original rate for now.
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Erasmo
2 years ago
I'm with the crowd on this one. Option D is the way to go. Though I do wonder if the audio quality will be as good as a human transcriptionist. Maybe we should hire some really bored linguists instead?
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Kasandra
2 years ago
User 3: Hiring linguists might be a good idea for quality control, but automating the transcription with Option D is more efficient.
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Lanie
2 years ago
I agree, using asynchronous recognition with the Speech-to-Text API will also speed up the transcription process.
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Katheryn
2 years ago
Option D is definitely the best choice. Upsampling the audio to 16 kHz will improve the transcription accuracy.
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Alba
2 years ago
That's a good point, but I think using the original rate is more efficient.
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Reta
2 years ago
But wouldn't upsampling to 16 kHz improve the transcription accuracy?
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Margurite
2 years ago
Haha, I'm just imagining the poor interns having to listen to all those long, boring call recordings. Good thing they've got the Speech-to-Text API to do the dirty work!
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Frederick
2 years ago
D) Upsample the audio recordings to 16 kHz. and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.
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Sueann
2 years ago
C) Upsample the audio recordings to 16 kHz. and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
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Maile
2 years ago
B) Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.
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Alyce
2 years ago
A) Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
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Galen
2 years ago
I agree with Alba, using the original rate seems like the best option.
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Curt
2 years ago
Option D all the way! 16 kHz audio and async recognition - that's the way to go. Gotta love those Google best practices, am I right?
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Lai
2 years ago
I agree with Antonio. The Google-recommended best practices suggest using asynchronous recognition for longer audio files, and upsampling to 16 kHz will improve the accuracy of the transcription.
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Antonio
2 years ago
Option D seems like the best choice here. Upsampling to 16 kHz and using asynchronous recognition will likely give us the best results for long audio recordings.
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Elli
2 years ago
Definitely, implementing this new feature will enhance the platform's capabilities for users.
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Dalene
2 years ago
It's important to follow best practices to ensure accurate transcriptions for future applications.
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Denny
2 years ago
Agreed, asynchronous recognition will also help with processing longer audio recordings more efficiently.
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Daniel
2 years ago
Great, let's go with option D then. It seems like the most effective approach for implementing the voice call transcription feature.
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Carmen
2 years ago
I agree, using asynchronous recognition will also help with processing longer audio recordings more efficiently.
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Kirk
2 years ago
I think option D is the way to go. Upsampling to 16 kHz should improve the transcription accuracy.
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Blossom
2 years ago
I think option D is the way to go. Upsampling to 16 kHz should improve transcription accuracy.
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Alba
2 years ago
I think we should use the original audio sampling rate for transcription.
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