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 a data pipeline with EC2, Kinesis, and S3, and using AWS Machine Learning Service to build a regression model. The tutorial explains data preparation, schema definition, and the distinction between categorical and numeric data types. It concludes with setting up and running the model in the AWS console.

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

• To analyze social media data

• To manage EC2 instances

• To predict order quantities

• To predict the weather

Which AWS service is primarily used to build regression models as discussed in the video?

• Amazon SageMaker

• Amazon Rekognition

• Amazon Machine Learning

• Amazon Comprehend

What type of data is required to be uploaded to S3 for the machine learning model?

• XML

• JSON

• CSV

• HTML

In the context of the video, what is the difference between categorical and numeric data?

• Categorical data has a meaningful order, numeric data does not

• Numeric data has a meaningful order, categorical data does not

• Both have meaningful order

• Neither have meaningful order

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

• SKU

• Description

• Quantity

• Invoice Number

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

• To provide context for predictions

• To increase data size

• To reduce processing time

• To improve model accuracy

What is the default evaluation setting for the machine learning model as mentioned in the video?

• 10%

• 20%

• 30%

• 40%