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A company is creating a model to label credit card transactions. The company has a large volume of sample transaction data to train the model. Most of the transaction data is unlabeled. The data does not contain confidential information. The company needs to obtain labeled sample data to fine-tune the model. Which solutions will meet these requirements? (Choose two.)
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Amazon SageMaker Ground Truth uses human labelers (including Amazon Mechanical Turk workers) to create labeled training datasets. Amazon SageMaker AI labeling jobs provide automated data labeling capabilities. Both options help obtain labeled data from unlabeled transaction data for fine-tuning.
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