MLS-C01 Question 296
Single answerA retail company wants to implement a personalized product recommendation system for its e-commerce platform. They have customer behavioral data, such as browsing history, purchase history, and demographics, stored in Amazon S3. The company needs a solution that minimizes development effort and allows them to scale easily with minimal infrastructure management. Which AWS service would be most appropriate for this use case?
- A
Amazon Personalize
- B
Amazon SageMaker
- C
AWS Glue
- D
Amazon Comprehend
Show answer and explanation
Correct answer: A
Explanation
Amazon Personalize is the most suitable choice because it is a fully managed service designed specifically for building real-time personalized recommendation systems. It automates data preprocessing, feature engineering, and model training, allowing the company to achieve their goal with minimal development effort and easy scalability. Other services, like Amazon SageMaker and AWS Glue, would require more custom work and are not specialized for recommendation tasks.
- A. Correct.
Amazon Personalize is a managed ML service specifically designed for building personalized recommendation systems. It requires minimal development effort and automates many aspects of the ML lifecycle, making it the most appropriate choice for this use case.
- B. Incorrect.
Amazon SageMaker is a general-purpose ML platform that would require significant custom development to build a recommendation system. While powerful, it doesn’t minimize development effort as much as Amazon Personalize.
- C. Incorrect.
AWS Glue is an ETL service for preparing and transforming data, but it is not designed for building machine learning models or recommendation systems.
- D. Incorrect.
Amazon Comprehend is a natural language processing service for extracting insights from text, and it is not relevant to building a recommendation system for this scenario.