AWS Certified Machine Learning – Specialty — Question 121

A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?

Answer options

• A. Use the event tracker in Amazon Personalize to include real-time user interactions.
• B. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
• C. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
• D. Add event type and event value fields to the interactions dataset in Amazon Personalize.

Correct answer: A

Explanation

The correct answer is A because using the event tracker allows the model to incorporate real-time user interactions, which can help improve the recommendations based on current user behavior. Options B and D do not address the need for real-time data, and option C suggests a different algorithm that may not solve the immediate issue of adapting to user interactions during the campaign.