No-Code Machine Learning Using Amazon AWS SageMaker Canvas - Building and Using the Model for Prediction - Project 1 - B

The video tutorial guides users through creating a model using Amazon SageMaker Canvas. It covers selecting and preparing datasets, choosing target columns, and configuring model columns. The tutorial explains the options for building models, including quick and standard builds, and demonstrates how to preview and analyze model accuracy using various metrics. The session concludes with a brief overview of the next steps in using test data for predictions.

What is the first step after creating a new model in SageMaker Canvas?

• Choose a model type

• Select a dataset

• Name the model

• Select a target column

What does SageMaker Canvas automatically recommend based on the target column?

• Column relevance

• Data distribution

• Model type

• Dataset type

Why might you exclude certain columns, like 'Student ID', from your model?

• They have too many missing values

• They are not unique

• They are not relevant to the prediction

• They are too complex

What is the main difference between a quick build and a standard build in SageMaker Canvas?

• Standard build is faster

• Quick build is more accurate

• Quick build prioritizes speed over accuracy

• Standard build cannot be shared

What feature provides an estimated accuracy and column impact analysis in SageMaker Canvas?

• Quick build

• Preview model

• Standard build

• Grid view

Which metric is a balanced measure of accuracy that considers class balance?

• Recall

• F1 Score

• Precision

• AUC

What can you download to get a visual representation of advanced metrics?

• A dataset

• A model summary

• An image

• A text file