MLA-C01 Question 155
Select 3You are working on an e-commerce platform and need to recommend products to users based on their past interactions. You decide to use Amazon Personalize to implement this functionality. Which of the following steps are required to set up and deploy a recommendation model using Amazon Personalize?
- A
Prepare and upload user-item interaction data to an Amazon S3 bucket.
- B
Use the Amazon SageMaker JumpStart feature to automatically train a recommendation model.
- C
Create a dataset group and import interaction data into Amazon Personalize.
- D
Train a solution version using a recipe that fits your use case.
- E
Deploy the trained model using Amazon Elastic Inference.
Show answer and explanation
Correct answers: A, C, D
Explanation
To build a recommendation system using Amazon Personalize, you need to prepare and upload user-item interaction data, create a dataset group, import the data, and train a solution version using a recipe that fits your use case. Amazon SageMaker JumpStart and Amazon Elastic Inference are not part of the Amazon Personalize workflow and are therefore not applicable to this scenario.
- A. Correct.
This is correct. Amazon Personalize requires user-item interaction data to be uploaded to an S3 bucket. This data is then imported into a dataset group.
- B. Incorrect.
This is incorrect. Amazon SageMaker JumpStart is not used for Amazon Personalize. Instead, Amazon Personalize has its own workflow for creating and training models.
- C. Correct.
This is correct. Creating a dataset group and importing interaction data is a required step for using Amazon Personalize.
- D. Correct.
This is correct. Training a solution version using an appropriate recipe is necessary to build a recommendation model in Amazon Personalize.
- E. Incorrect.
This is incorrect. Amazon Elastic Inference is not used in Amazon Personalize. Instead, you deploy the solution version within Amazon Personalize itself.