MLA-C01 Question 66
Select 3You are working on a machine learning project to predict customer churn. The dataset is unstructured and contains text fields, missing values, and categorical columns. Which of the following AWS services or features can help you clean, transform, and prepare the data efficiently for your machine learning model?
- A
Amazon SageMaker Data Wrangler
- B
AWS Glue DataBrew
- C
Amazon Rekognition
- D
AWS Glue
- E
Amazon Comprehend
Show answer and explanation
Correct answers: A, B, D
Explanation
To clean, transform, and prepare data for a machine learning model, tools like Amazon SageMaker Data Wrangler, AWS Glue DataBrew, and AWS Glue are ideal. They provide the capabilities to handle missing values, transform categorical data, and process unstructured datasets. Amazon Rekognition and Amazon Comprehend, while powerful, are specialized for image/video analysis and text insights, respectively, and are not appropriate for general data preparation tasks.
- A. Correct.
Amazon SageMaker Data Wrangler is designed for data preparation, including handling missing values and transforming features, making it suitable for this task.
- B. Correct.
AWS Glue DataBrew can be used to visually clean and normalize data, making it an excellent choice for preparing structured datasets.
- C. Incorrect.
Amazon Rekognition is used for image and video analysis and is not relevant for cleaning or preparing tabular data.
- D. Correct.
AWS Glue is a fully managed ETL (Extract, Transform, Load) service that can help you transform unstructured data into a structured format and handle large-scale data preparation tasks.
- E. Incorrect.
Amazon Comprehend is a natural language processing service for extracting insights from text, but it is not used for general data cleaning or preparation.