MLA-C01 Question 65
Single answerA data science team is working on preparing a dataset for a machine learning model. The dataset contains missing values, inconsistent formats, and outliers. The team wants to explore and visualize the data, perform transformations such as filling missing values and correcting inconsistent formats, and then export the cleaned data for training in Amazon SageMaker. Which AWS service or combination of services would best help the team achieve these tasks?
- A
AWS Glue
- B
Amazon SageMaker Data Wrangler
- C
AWS Glue DataBrew
- D
Amazon QuickSight
Show answer and explanation
Correct answer: B
Explanation
The team’s requirements include exploring, visualizing, and transforming data, as well as integrating the cleaned data with an Amazon SageMaker machine learning workflow. Amazon SageMaker Data Wrangler is built specifically for these tasks, providing a seamless experience for ML data preparation. While AWS Glue DataBrew and AWS Glue are useful for certain parts of the workflow, they are not as tailored to the ML-specific data preparation process as SageMaker Data Wrangler.
- A. Incorrect.
AWS Glue is primarily an ETL (Extract, Transform, Load) service designed for large-scale data integration tasks. While it can transform data, it lacks the direct visualization and exploration capabilities needed for this scenario.
- B. Correct.
Amazon SageMaker Data Wrangler is specifically designed for exploring, visualizing, transforming, and preparing data for machine learning workflows. It provides a user-friendly interface for tasks like handling missing values, correcting formats, and outlier detection, making it the best fit for this use case.
- C. Incorrect.
AWS Glue DataBrew is another data preparation tool, but it is more focused on data cleansing and transformations and lacks the deeper integration with SageMaker for ML-specific workflows. While it could be used, it is not the most streamlined solution for this scenario.
- D. Incorrect.
Amazon QuickSight is a business intelligence tool used for creating visual dashboards and insights from data but is not designed for data preparation, transformation, or direct ML workflow integration.