You work as a machine learning specialist for a retail pet products chain. Your company is trying to use machine learning to help determine the breed of dogs in the photos your customers tag on Instagram and Twitter. You need to build a machine learning model to accomplish this problem.
Which SageMaker model would you use to fit your machine learning problem best?

Answer options:

A.K-Means
B.Image Classification
C.Sequence-to-Sequence
D.Neural Topic Model

Answer: B
Option A is incorrect. K-Means is used to find discrete groupings in data. It is mostly used on numeric data that is continuous. Image data is not numeric and is not continuous, so K-Means would not be a good model for your dog image classification problem. (See the Amazon SageMaker developer guide titled K-Means Algorithm)
Option B is correct. The Image Classification model is used to solve classification problems such as image classification. (See the Amazon SageMaker developer guide titled Image Classification Algorithm)
Option C is incorrect. The Sequence-to-Sequence model is used to take a sequence of tokens and produces another sequence of tokens. It is used for problems like language translation, text summarization, and speech-to-text. (See the Amazon SageMaker developer guide titled Sequence-to-Sequence Algorithm)
Option D is incorrect. The Neural Topic Model algorithm is used to organize documents into topics. This type of model is not suited to image classification. (See the Amazon SageMaker developer guide titled Neural Topic Model (NTM) Algorithm)
Reference:
Please see the Amazon SageMaker developer guide titled Use Amazon SageMaker Built-in Algorithms.