Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 210 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 210
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A retail company wants to build a recommendation system for the company's website. The system needs to provide recommendations for existing users and needs to base those recommendations on each user's past browsing history. The system also must filter out any items that the user previously purchased.

Which solution will meet these requirements with the LEAST development effort?

A. Train a model by using a user-based collaborative filtering algorithm on Amazon SageMaker. Host the model on a SageMaker real-time endpoint. Configure an Amazon API Gateway API and an AWS Lambda function to handle real-time inference requests that the web application sends. Exclude the items that the user previously purchased from the results before sending the results back to the web application.
B. Use an Amazon Personalize PERSONALIZED_RANKING recipe to train a model. Create a real-time filter to exclude items that the user previously purchased. Create and deploy a campaign on Amazon Personalize. Use the GetPersonalizedRanking API operation to get the real-time recommendations.
C. Use an Amazon Personalize USER_PERSONALIZATION recipe to train a model. Create a real-time filter to exclude items that the user previously purchased. Create and deploy a campaign on Amazon Personalize. Use the GetRecommendations API operation to get the real-time recommendations.
D. Train a neural collaborative filtering model on Amazon SageMaker by using GPU instances. Host the model on a SageMaker real-time endpoint. Configure an Amazon API Gateway API and an AWS Lambda function to handle real-time inference requests that the web application sends. Exclude the items that the user previously purchased from the results before sending the results back to the web application.

Show Suggested Answer

Suggested Answer: C 🗳️

by damaldon at Feb. 9, 2023, 10:34 a.m.

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Comments

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loict

9 months, 3 weeks ago

Selected Answer: C

A. NO - we want to leverage a prebuilt model for efficiency B. NO - PERSONALIZED_RANKING uses a predefined list of items as input C. YES - USER_PERSONALIZATION uses past user history as input D. NO - we want to leverage a prebuilt model for efficiency

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Mickey321

10 months, 2 weeks ago

Selected Answer: C

Answer C

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Richaqua

11 months, 4 weeks ago

User personalization: Recommendations tailored to a user’s profile, behavior, preferences, and history. This is most commonly used to boost customer engagement and satisfaction. It can also drive higher conversion rates. Personalized ranking: Items re-ranked in a category or search response based on user preference or history. This use case is used to surface relevant items or content to a specific user ensuring a better customer experience. Amazon Personalize supports re-ranking while optimizing for business priorities such as revenue, promotions, or trending items. https://aws.amazon.com/personalize/faqs/

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Mllb

1 year, 2 months ago

Selected Answer: B

B it,s the correct answer

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blanco750

1 year, 3 months ago

Selected Answer: C

C looks the right choice

upvoted 3 times

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pan_b

1 year, 3 months ago

Selected Answer: C

Its C, User Personalizationis recommended for user interaction scenarios https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html

upvoted 3 times

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Valcilio

1 year, 3 months ago

Selected Answer: C

It's C, User Personalization is recommended for self-user user case.

upvoted 2 times

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Chelseajcole

1 year, 3 months ago

Selected Answer: C

The User-Personalization (aws-user-personalization) recipe is optimized for all personalized recommendation scenarios. It predicts the items that a user will interact with based on Interactions, Items, and Users datasets. When recommending items, it uses automatic item exploration.

upvoted 2 times

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GiyeonShin

1 year, 4 months ago

Selected Answer: C

Option B: https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-search.html "With Personalized-Ranking, you must manually create a new solution version (retrain the model) to reflect updates to your catalog and update the model with your user’s most recent behavior." Option B has a disadvantage to update the catalog in retail company. So, Option C has the less effort to oprate than Option B

upvoted 4 times

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AjoseO

1 year, 4 months ago

Selected Answer: B

Option B is a better fit for the given requirements since it specifically mentions the need to filter out items that the user has previously purchased. The PERSONALIZED_RANKING recipe in Amazon Personalize is designed to provide personalized recommendations while allowing for exclusion of previously purchased items using a filter. In contrast, the USER_PERSONALIZATION recipe in option C is designed to provide personalized recommendations without the ability to filter out previously purchased items. Therefore, option B is the best choice for meeting the given requirements with the least development effort.

upvoted 3 times

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damaldon

1 year, 4 months ago

Answer is C https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html

upvoted 4 times

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damaldon

1 year, 4 months ago

Answer is C https://docs.aws.amazon.com/personalize/latest

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