AWS Certified Data Analytics Specialty 2021 – Hands-On - (Exercise) Amazon Machine Learning, Part 1

The video tutorial guides users through predicting order quantities using AWS services. It covers setting up Amazon Machine Learning, uploading and configuring schema files in S3, and understanding data types. The tutorial emphasizes the use of regression models and explains the distinction between categorical and numeric data. Finally, it walks through the final steps to complete the machine learning model setup.

What is the primary purpose of using Amazon Machine Learning in the context of the video?

• To predict order quantities for new orders

• To manage user accounts in AWS

• To categorize different types of products

• To enhance the security of AWS services

Which AWS service is used to collect and store order data before it is processed by Amazon Machine Learning?

• Amazon DynamoDB

• Amazon RDS

• Amazon Kinesis Data Firehose

• Amazon EC2

What type of models does Amazon's machine learning service primarily support?

• Clustering Models

• Regression Models

• Support Vector Machines

• Decision Trees

Why is an invoice number considered a categorical attribute in the schema?

• Because it is used to calculate order trends

• Because it is always a numeric field

• Because it is a text field

• Because it represents a unique category without numerical significance

What is the role of a schema file in Amazon S3?

• To manage user permissions

• To encrypt the data

• To define the structure and data types of the dataset

• To store raw data

What is the target variable in the machine learning model discussed in the video?

• Unit Price

• Description

• Quantity

• SKU

What is the purpose of a row identifier in the machine learning model setup?

• To improve model accuracy

• To provide context for predictions

• To encrypt the data

• To categorize the data