A team will preprocess text with part-of-speech tagging and key phrase extraction, then feed the processed text into a custom classifier already implemented and trained with Apache MXNet. Which approach lets the team deliver this solution most quickly?

The correct approach leverages Amazon Comprehend for its managed NLP services (part-of-speech tagging and key phrase extraction), which are quick to integrate. It then uses AWS Deep Learning Containers (DLCs) within SageMaker to deploy the existing custom MXNet classifier. This is the fastest method because it reuses the pre-trained custom classifier without modification and utilizes SageMaker's managed deployment capabilities for MXNet models. Using Amazon Comprehend for classification is incorrect because the team already has a custom classifier. Using an NLP library in SageMaker for part-of-speech tagging would require more setup than using Comprehend directly. Using SageMaker's built-in LDA algorithm is incorrect because the team has a custom classifier implemented in MXNet, and LDA is a topic modeling algorithm, not a general classifier.