MLS-C01 Question 106
Select 3You are working on an ML project for a retail company, and you have a large dataset containing customer transactions. You need to understand the distribution of sales across different product categories and identify any outliers in the transaction amounts before feeding the data into a machine learning model. Which of the following AWS services or tools should you use to analyze and visualize the data effectively?
- A
Amazon QuickSight
- B
AWS Glue DataBrew
- C
Amazon SageMaker Clarify
- D
Amazon Athena
- E
Amazon Rekognition
Show answer and explanation
Correct answers: A, B, D
Explanation
To analyze and visualize data for machine learning, you can use a combination of tools depending on your requirements. Amazon QuickSight helps create visualizations to explore data distributions, AWS Glue DataBrew is ideal for cleaning and profiling data to identify outliers, and Amazon Athena can be used to query and filter the data effectively. SageMaker Clarify and Rekognition are not appropriate for this specific use case.
- A. Correct.
Amazon QuickSight is a business intelligence tool that enables you to create visualizations and dashboards to analyze data, making it suitable for understanding the distribution of sales across product categories.
- B. Correct.
AWS Glue DataBrew is a data preparation tool that allows you to clean, normalize, and visualize data quickly, making it an excellent choice for identifying outliers and understanding data patterns.
- C. Incorrect.
Amazon SageMaker Clarify is primarily used for identifying bias in datasets and models, not for general data analysis and visualization tasks.
- D. Correct.
Amazon Athena is a serverless query service that allows you to use SQL to analyze structured data in S3, which can help with querying and understanding the dataset.
- E. Incorrect.
Amazon Rekognition is an image and video analysis service and is not relevant for analyzing or visualizing tabular customer transaction data.