Question 1 of 30
A retail company is looking to enhance its customer experience by implementing a recommendation system using AWS services. They have a dataset containing customer purchase history, product ratings, and demographic information. The company wants to utilize Amazon SageMaker to build a machine learning model that predicts which products a customer is likely to purchase next. Which approach should the company take to ensure that their model is both accurate and scalable?
Use Amazon SageMaker's built-in algorithms for collaborative filtering and train the model on the entire dataset, then deploy it as a real-time endpoint for predictions.
Manually code a recommendation algorithm from scratch and run it on an EC2 instance to handle the predictions.
Use Amazon SageMaker to create a batch transform job that processes the data periodically and updates the recommendations weekly.
Implement a simple heuristic-based approach that recommends products based on the most popular items in the dataset without using machine learning.

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