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MLS-C01 · Question #230
MLS-C01 Question #230: Real Exam Question with Answer & Explanation
The correct answer is D: Use SageMaker script mode, and use train.py unchanged. Put the TFRecord data into an. TFRecord could be uploaded to S3 directly and be used as SageMaker's data source.
ML Implementation and Operations
Question
A company's machine learning (ML) specialist is designing a scalable data storage solution for Amazon SageMaker. The company has an existing TensorFlow-based model that uses a train.py script. The model relies on static training data that is currently stored in TFRecord format. What should the ML specialist do to provide the training data to SageMaker with the LEAST development overhead?
Options
- APut the TFRecord data into an Amazon S3 bucket. Use AWS Glue or AWS Lambda to reformat
- BRewrite the train.py script to add a section that converts TFRecord data to protobuf format. Point
- CUse SageMaker script mode, and use train.py unchanged. Point the SageMaker training
- DUse SageMaker script mode, and use train.py unchanged. Put the TFRecord data into an
Explanation
TFRecord could be uploaded to S3 directly and be used as SageMaker's data source.
Topics
#SageMaker Script Mode#Data Ingestion#TFRecord#Amazon S3
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