AIF-C01 Question #139: Real Exam Question with Answer & Explanation

Question

A company wants to create an ML model to predict customer satisfaction. The company needs fully automated model tuning. Which AWS service meets these requirements?

Options

• AAmazon Personalize
• BAmazon SageMaker
• CAmazon Athena
• DAmazon Comprehend

Explanation

Amazon SageMaker is the correct choice because it provides a comprehensive ML platform with Automatic Model Tuning (Hyperparameter Optimization), which fully automates the process of finding the best model configuration to optimize performance metrics like customer satisfaction predictions.

Why the distractors are wrong:

• Amazon Personalize (A) is a managed service specifically designed for recommendation systems (e.g., product recommendations), not general-purpose ML model building with custom tuning.
• Amazon Athena (C) is a serverless query service for analyzing data in S3 using SQL - it has no ML model training or tuning capabilities.
• Amazon Comprehend (D) is an NLP service for text analysis tasks like sentiment detection and entity recognition, but it doesn't allow you to build and tune custom ML models for arbitrary use cases like customer satisfaction prediction.

Memory Tip: Think of SageMaker as the "Swiss Army knife" of AWS ML services - whenever a question mentions building, training, deploying, or tuning a custom ML model end-to-end, SageMaker is almost always the answer. The keyword "automated model tuning" directly maps to SageMaker's Hyperparameter Tuning jobs.

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