Exam AWS Certified Machine Learning - Specialty topic 1 question 263 discussion

A. LDA is designed to discover abstract topics in a collection of documents. It is commonly used for topic modeling and is one of the most popular techniques for extracting topics from text data. C. NTM is also used in topic modeling. It uses deep learning to discover topics in a collection of documents, and it can produce similar results to LDA but potentially with better accuracy due to its neural network foundation. Incorrect choices: B. Random forest classifier is a classification algorithm. It is better suited for classification tasks based on labeled data. D. SVM is also a classification algorithm. It works well for binary classification problems. E. Linear regression is a regression algorithm used to predict continuous values. It's not suitable for topic modeling.

LDA and NTM are the only applicable options for topic modelling here

A. YES B. NO - for classification C. YES D. NO - for classification E. NO - for classification

both unsupervised learning algorithms that can discover abstract topics in a collection of text documents . These algorithms can help the data scientist to analyze the audit documents and provide a list of the top words for each category to help the auditors assess the relevance of the topic. LDA and NTM are different from other algorithms that are not suitable for this scenario, such as:

AC for topics

Although you can use both the Amazon SageMaker NTM and LDA algorithms for topic modeling, they are distinct algorithms and can be expected to produce different results on the same input data. A and C https://docs.aws.amazon.com/sagemaker/latest/dg/ntm.html https://docs.aws.amazon.com/sagemaker/latest/dg/lda.html