You work for an online retailer as a machine learning specialist. Your team has been tasked with creating a machine learning model to identify similar products for a product comparison chart on many of the product pages on your website. Your website designers want to show a grid of a product compared to similar products, even products from competitors. The grid will show the price, review summary (stars), and key features of each product. You are at the stage in your development where you are gathering, cleaning, and transforming your data and training your model.
Using machine learning techniques, how can you determine similar product data for use in this grid in the most efficient manner?

Answer options:

A.Use the Linear Learner built-in SageMaker algorithm and set its predictor_type hyperparameter to binary_classifier.
B.Use the XGBoost built-in SageMaker algorithm and set its objective hyperparameter to reg:logistic.
C.Use the Linear Learner built-in SageMaker algorithm and set its predictor_type hyperparameter to regressor.
D.Use the AWS LakeFormation FindMatches ML Transform 
E.Use the XGBoost built-in SageMaker algorithm and set its objective hyperparameter to reg:linear.
F.Use the AWS Glue FindMatches ML Transform and set its precision-recall parameter to recall.

Answer: D
Option A is incorrect. Using a Linear Learner algorithm-based model with the binary_classifier predictor_type may help you find similar products, but it is not the most efficient technique listed in the options.
Option B is incorrect. Using the XGBoost algorithm-based model with the reg:logistic objective may help you find similar products, but it is not the most efficient technique listed in the options.
Option C is incorrect. Using the Linear Learner algorithm with the regressor predictor_type would not be a good choice for a discrete categorization problem such as matching similar products.
Option D is correct. The LakeFormation FindMatches transformation can be used to find similar products in your data stores and even external data sources, such as those of competitor products.
Option E is incorrect. Using the XGBoost algorithm with the reg:linear objective would not be a good choice for a discrete categorization problem such as matching similar products.
Option F is incorrect. The AWS Glue FindMatches ML Transform uses machine learning capabilities to find matching records in your database, even when the records don’t have exactly matching fields. Setting the FindMatches ML Transform precision_recall parameter to recall is incorrect since this setting is used when you want to minimize false negatives. Meaning, the ML transform failed to find a match when a match actually existed. This is not an optimal result, but it is a better outcome than incorrectly identifying two items as similar when they really aren’t (false positive).
Reference:
Please see the AWS Glue developer guide titled Machine Learning Transforms in AWS Glue, the AWS Glue developer guide titled Tuning Machine Learning Transforms in AWS Glue, and the Amazon SageMaker developer guide titled Use Amazon SageMaker Built-in Algorithms, and the Amazon Glue Developer guide titled Matching Records with AWS Lake Formation FindMatches.