MLS-C01 exam dumps

MLS-C01 practice question 9 of 389

AWS Certified Machine Learning - Specialty. Expert level, Amazon Web Services. Free question with the correct answer and a full explanation.

MLS-C01 Question 9

Select 3

A company is building a recommendation system for its e-commerce platform and wants to identify the most appropriate data sources to improve the relevance of its recommendations. Which data sources should the company prioritize for this use case?

  1. A

    User transaction history from the e-commerce platform

  2. B

    Real-time weather data from third-party APIs

  3. C

    Product metadata, such as categories, descriptions, and pricing

  4. D

    Geolocation data of users when they browse the platform

  5. E

    Social media sentiment data about the company

Show answer and explanation

Correct answers: A, C, D

Explanation

To build an effective recommendation system, the company should focus on user-specific and product-specific data such as transaction history, product metadata, and geolocation data. These data sources provide actionable insights into user preferences and product relationships, which are essential for generating relevant recommendations.

  • A. Correct.

    User transaction history is a primary data source for recommendations, as it provides direct insights into the user's preferences and purchasing behavior.

  • B. Incorrect.

    Real-time weather data is less relevant for a general e-commerce recommendation system unless the platform specifically sells weather-sensitive products.

  • C. Correct.

    Product metadata is crucial for a recommendation system because it helps in understanding the relationships between products and their features.

  • D. Correct.

    Geolocation data can add valuable context to recommendations, such as suggesting region-specific products or tailoring availability based on location.

  • E. Incorrect.

    Social media sentiment data is not directly relevant for building a recommendation system, as it focuses on general sentiment rather than individual user preferences or behavior.

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