You are a machine learning specialist at a large online retailer. Your team is working on a recommender model for your online purchase workflow. The recommender will suggest similar items to the items the user has viewed or placed in their shopping cart. To find items that are similar to the item your customer is viewing, you want to compare other users who like each item. If these similar users like the same two items, then the probability the items are similar is higher.
Which Amazon SageMaker built-in algorithm is best suited to your use case?

Answer options:

A.Semantic Segmentation
B.K-Nearest Neighbor
C.Linear Learner
D.Random Cut Forest

Answer: B
Option A is incorrect. The semantic segmentation algorithm is used to develop computer vision applications. You are trying to find items that are similar to each other. 
Option B is correct. The k-nearest neighbor algorithm is used to find items that are similar to each other. This is what you need to find similar items to recommend to a user in the online purchase workflow.
Option C is incorrect. The linear learner algorithm is used to show how a change in an independent variable affects a dependent variable. You are trying to find items that are similar to each other.
Option D is incorrect. The random cut forest algorithm is predominantly used to classify observations, such as whether a transaction is fraudulent or not. You are trying to find items that are similar to each other.
Reference:
Please see the Amazon SageMaker developer guide titled
Use Amazon Sagemaker Built-in Algorithms