AWS Certified Machine Learning – Specialty — Question 179

A data science team is planning to build a natural language processing (NLP) application. The application's text preprocessing stage will include part-of-speech tagging and key phase extraction. The preprocessed text will be input to a custom classification algorithm that the data science team has already written and trained using Apache MXNet.
Which solution can the team build MOST quickly to meet these requirements?

Answer options

• A. Use Amazon Comprehend for the part-of-speech tagging, key phase extraction, and classification tasks.
• B. Use an NLP library in Amazon SageMaker for the part-of-speech tagging. Use Amazon Comprehend for the key phase extraction. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.
• C. Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use Amazon SageMaker built-in Latent Dirichlet Allocation (LDA) algorithm to build the custom classifier.
• D. Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.

Correct answer: D

Explanation

Option D is correct because it effectively uses Amazon Comprehend for both part-of-speech tagging and key phrase extraction, which are quick and reliable services. Additionally, it allows for the use of AWS Deep Learning Containers with Amazon SageMaker to create the custom classifier, aligning with the team's existing work. The other options either complicate the solution unnecessarily or do not utilize Amazon Comprehend effectively for the tasks required.