MLA-C01 Question 79
Single answerA data science team at a retail company is developing a machine learning model to predict customer preferences. They need a high-quality labeled dataset that includes customer reviews categorized into sentiment labels (positive, neutral, negative). The team wants to use an AWS service to efficiently manage the labeling process while leveraging automation and human reviewers for quality assurance. Which AWS service should they use?
- A
Amazon SageMaker Ground Truth
- B
Amazon Rekognition
- C
Amazon Comprehend
- D
AWS Data Pipeline
Show answer and explanation
Correct answer: A
Explanation
Amazon SageMaker Ground Truth is specifically designed to help create high-quality labeled datasets. It provides automated labeling powered by machine learning, along with the option to include human reviewers for complex tasks or quality assurance. This makes it the best choice for the data science team to label customer reviews with sentiment categories.
- A. Correct.
Amazon SageMaker Ground Truth is the correct service for creating high-quality labeled datasets. It supports automated data labeling and human review workflows, making it ideal for tasks like sentiment analysis.
- B. Incorrect.
Amazon Rekognition is used for analyzing images and videos, such as facial recognition and object detection. It is not suited for sentiment analysis or labeling text data.
- C. Incorrect.
Amazon Comprehend is a natural language processing service that can analyze sentiment in text, but it is not a data labeling or annotation tool.
- D. Incorrect.
AWS Data Pipeline is a service to automate data movement and transformation across AWS resources but is not related to data labeling or annotation.