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Exam AWS Certified Machine Learning - Specialty topic 1 question 177 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 177
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A retail company wants to update its customer support system. The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category.
Currently, an operator manually performs the category assignment and routing. After the operator classifies and routes the claim, the company stores the claim's record in a central database. The claim's record includes the claim's category.
The company has no data science team or experience in the field of machine learning (ML). The company's small development team needs a solution that requires no ML expertise.
Which solution meets these requirements?

A. Export the database to a .csv file with two columns: claim_label and claim_text. Use the Amazon SageMaker Object2Vec algorithm and the .csv file to train a model. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.
B. Export the database to a .csv file with one column: claim_text. Use the Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm and the .csv file to train a model. Use the LDA algorithm to detect labels automatically. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.
C. Use Amazon Textract to process the database and automatically detect two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the extracted information to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.
D. Export the database to a .csv file with two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the .csv file to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.

Show Suggested Answer

Suggested Answer: D 🗳️

by cron0001 at April 29, 2022, 10:13 a.m.

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Comments

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cron0001

Highly Voted 2 years, 2 months ago

Selected Answer: D

I would say D. We shouldn't need Textract to extract columns from a database

upvoted 16 times

Omijh

2 years, 1 month ago

I think D because Textract doesn't support CSV but only PNG, JPEG, TIFF, and PDF formats

upvoted 6 times

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ovokpus

Highly Voted 2 years ago

Selected Answer: D

D because it does not require heavy machine learning expertise

upvoted 5 times

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loict

Most Recent 9 months, 3 weeks ago

Selected Answer: D

A. NO - Object2Vec is unsupervised, it will create vector representations but not assign to a category the claims B. NO - we want a supervised method, LDA will create topics in an unsupervised way C. NO - again we want a supervised method D. YES - That is supervised; no need for ML skills, only basic API programming

upvoted 4 times

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kaike_reis

10 months, 3 weeks ago

C is wrong because the columns doesn't exist.

upvoted 1 times

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Mickey321

11 months ago

Selected Answer: D

D. Export the database to a .csv file with two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the .csv file to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.

upvoted 1 times

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AjoseO

1 year, 4 months ago

Selected Answer: D

Option D meets the requirements. The solution requires no ML expertise, and the small development team can use the Amazon Comprehend custom classification API to train a model to automatically detect claim categories. The company can export the database to a .csv file with two columns: claim_label and claim_text. Then, the development team can use the .csv file to train the custom classifier. Finally, the team can develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue. This solution is straightforward, does not require extensive expertise, and can be implemented quickly.

upvoted 1 times

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DD4

1 year, 10 months ago

Selected Answer: A

It should A because Object2Vec is meant for text classification. The problem is to categorize the text based on the content.

upvoted 1 times

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s12201812

1 year, 10 months ago

B. LDA is for topic modelling based on categories. Comprehend is for extracting the entities related to sentiments etc.

upvoted 1 times

GiyeonShin

1 year, 4 months ago

Comprehend can be used for custom classification of NLP too(https://aws.amazon.com/ko/comprehend/features/). LDA can find document topics and word distribution for topics, but it is necessary to manually link the topics with predefined customer category.

upvoted 1 times

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hamimelon

1 year, 6 months ago

But it says the solution should not require ML expertise. LDA requires ML expertise.

upvoted 1 times

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Morsa

1 year, 11 months ago

The firm needs a solution for their manual process and this means among others the routing of their client orders. To do that you will need textract

upvoted 2 times

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