MLS-C01 · Question #70
An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim…
The correct answer is D. Apply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features. Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word…
Question
An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not. Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task?
Options
- ADerive a dictionary of tokens from claims in the entire dataset. Apply one-hot encoding to tokens
- BApply Amazon SageMaker BlazingText in Word2Vec mode to claims in the training set. Send the
- CApply Amazon SageMaker BlazingText in classification mode to labeled claims in the training set
- DApply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features
How the community answered
(25 responses)- A24% (6)
- B8% (2)
- C12% (3)
- D56% (14)
Explanation
Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word level, Object2Vec needs be used instead of Word2Vec.
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