MLS-C01 · Question #123
A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static…
The correct answer is B. Use Amazon SageMaker script mode and use train.py unchanged. filenames = ["s3://bucketname/path/to/file1.tfrecord", "s3://bucketname/path/to/file2.tfrecord"] dataset = tf.data.TFRecordDataset(filenames)
Question
A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords. Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?
Options
- AUse Amazon SageMaker script mode and use train.py unchanged.
- BUse Amazon SageMaker script mode and use train.py unchanged.
- CRewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the
- DPrepare the data in the format accepted by Amazon SageMaker.
How the community answered
(48 responses)- A2% (1)
- B85% (41)
- C4% (2)
- D8% (4)
Explanation
filenames = ["s3://bucketname/path/to/file1.tfrecord", "s3://bucketname/path/to/file2.tfrecord"] dataset = tf.data.TFRecordDataset(filenames)
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