AWS Certified Machine Learning - Specialty MLS-C01 — Topic 1 Question 165

Question

A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as "a," "an," and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords. What should the data scientist do to meet these requirements?

Accepted Answer

Correct answer: D. The correct answer is D. Option D: Remove the stopwords from the blog post data by using the CountVectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer. Amazon S3 provides durable, scalable object storage that is fully managed. Amazon RDS is a managed relational database that handles patching, backups and failover.

AWS Certified Machine Learning - Specialty MLS-C01 — Question 165