A data scientist used the SageMaker Neural Topic Model (NTM) to recommend tags for blog posts stored as JSON in S3. The model suggests stopwords like "a," "an," and "the," and sometimes rare but valid words. The team wants to keep the rare words but ensure stopwords are not recommended. What should the data scientist do?

The correct solution involves preprocessing the text data to remove stopwords before training the NTM model. scikit-learn's CountVectorizer offers a stopwords parameter that can be set to 'english' or a custom list, effectively removing common words like "a," "an," and "the" during tokenization. Replacing the S3 data with these vectorized results ensures the NTM model trains on more meaningful terms. Calling Amazon Comprehend entity recognition is incorrect because it focuses on identifying entities (people, places, organizations) rather than removing general stopwords. Running PCA is a dimensionality reduction technique, not a method for text preprocessing or stopword removal. Using the SageMaker built-in Object Detection algorithm is entirely inappropriate as it is designed for image analysis, not text processing or topic modeling.