Topic 1 Question 260
A financial company sends special offers to customers through weekly email campaigns. A bulk email marketing system takes the list of email addresses as an input and sends the marketing campaign messages in batches. Few customers use the offers from the campaign messages. The company does not want to send irrelevant offers to customers.
A machine learning (ML) team at the company is using Amazon SageMaker to build a model to recommend specific offers to each customer based on the customer's profile and the offers that the customer has accepted in the past.
Which solution will meet these requirements with the MOST operational efficiency?
Use the Factorization Machines algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker endpoint to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
Use the Neural Collaborative Filtering algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker endpoint to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
Use the Neural Collaborative Filtering algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker batch inference job to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
Use the Factorization Machines algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker batch inference job to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
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コメント(9)
- 正解だと思う選択肢: D
D makes more sence to me. Collaborative filtering takes into account other users preferences which is what we want to avoid because we do not want irrelevant promotions
👍 3endeesa2023/11/28 - 正解だと思う選択肢: D
: Use the Factorization Machines algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker batch inference job to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system
👍 2Mickey3212023/08/03 From Chat GPT The solution that will meet the requirements with the MOST operational efficiency is option C: Use the Neural Collaborative Filtering algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker batch inference job to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
By using the Neural Collaborative Filtering algorithm, the ML team can build a model that can provide personalized offer recommendations based on customer profiles and past accepted offers. Deploying a SageMaker batch inference job allows for efficient processing of a large batch of customer data to generate offer recommendations. These recommendations can then be fed directly into the bulk email marketing system, streamlining the process and improving operational efficiency.
👍 2strike3test2023/08/14
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