Q68 — AWS AIF-C01 Ch.2

Question 68 of 100 | ← Chapter 2

A company operating an online learning platform wants to build a recommendation engine that recommends relevant courses based on user interests and learning history. Which AWS service meets the requirements for building and deploying such a recommendation engine?

Correct Answer: A. Amazon SageMaker

Explanation

A. Amazon SageMaker is the most suitable AWS service for building and deploying a recommendation engine. SageMaker provides fully managed infrastructure for end-to-end ML workflows—including built-in algorithms (e.g., factorization machines, neural collaborative filtering), Jupyter notebooks, automated model tuning, and scalable deployment—making it ideal for developing personalized recommendation systems. B. AWS Lambda is serverless compute for short-lived functions—not suited for long-running ML model training or serving. C. Amazon Rekognition analyzes visual content and has no relevance to recommendation logic. D. Amazon Comprehend performs NLP tasks on text—useful for content analysis but insufficient for building full recommendation engines requiring collaborative filtering or embedding-based personalization.