MLS-C01 · Question #224
MLS-C01 Question #224: Real Exam Question with Answer & Explanation
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Question
A company is using Amazon SageMaker to build a machine learning (ML) model to predict customer churn based on customer call transcripts. Audio files from customer calls are located in an on-premises VoIP system that has petabytes of recorded calls. The on-premises infrastructure has high-velocity networking and connects to the company's AWS infrastructure through a VPN connection over a 100 Mbps connection. The company has an algorithm for transcribing customer calls that requires GPUs for inference. The company wants to store these transcriptions in an Amazon S3 bucket in the AWS Cloud for model development. Which solution should an ML specialist use to deliver the transcriptions to the S3 bucket as quickly as possible?
Options
- AOrder and use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module
- BOrder and use an AWS Snowcone device with Amazon EC2 Inf1 instances to run the
- COrder and use AWS Outposts to run the transcription algorithm on GPU-based Amazon EC2
- DUse AWS DataSync to ingest the audio files to Amazon S3. Create an AWS Lambda function to
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