MLA-C01 Question 77
Single answerYou are building a machine learning model for sentiment analysis on customer feedback. To train your model, you need a labeled dataset where each piece of feedback is annotated with the corresponding sentiment (positive, negative, or neutral). You are considering using an AWS service to create this high-quality labeled dataset. Which AWS service or feature should you use to achieve this?
- A
Amazon SageMaker Ground Truth
- B
Amazon Comprehend
- C
Amazon Rekognition
- D
AWS DataSync
Show answer and explanation
Correct answer: A
Explanation
Amazon SageMaker Ground Truth is specifically designed for creating high-quality labeled datasets and supports various labeling tasks, including text classification for sentiment analysis. This makes it the ideal choice for the given scenario. Other services like Amazon Comprehend and Amazon Rekognition are specialized for specific tasks but do not provide labeling functionality, while AWS DataSync is unrelated to data annotation.
- A. Correct.
Amazon SageMaker Ground Truth is a managed data labeling service that provides tools to create high-quality labeled datasets for machine learning models. It supports tasks like text classification, which is relevant for sentiment analysis.
- B. Incorrect.
Amazon Comprehend is a natural language processing service that can perform sentiment analysis directly but is not designed for labeling datasets for training custom machine learning models.
- C. Incorrect.
Amazon Rekognition is a computer vision service for analyzing images and videos. It is not relevant for text-based sentiment analysis or dataset labeling.
- D. Incorrect.
AWS DataSync is a data transfer service used to move data between on-premises storage and AWS. It does not provide functionality for data annotation or labeling.