AIncorrect
Amazon SageMaker Feature Store
Feature Store is a repository that stores, shares, and reuses features.
It is not a pipeline feature that automates the entire workflow, so it is incorrect.
An ML team wants to build and run a series of machine learning workflow steps, such as data preparation, training, evaluation, and deployment, as an automated and reproducible pipeline. Which SageMaker feature is the MOST suitable?
1 / 1
Select an answer
CorrectB
Explanation
Choosing the pipeline feature that automates an ML workflow.
- 1「a series of machine learning workflow steps」Multiple steps from preparation to deployment
- 2「an automated and reproducible pipeline」Workflow automation = SageMaker Pipelines
BCorrect
Amazon SageMaker Pipelines
Correct. SageMaker Pipelines is a feature that defines and runs steps such as data preparation, training, evaluation, and deployment as an automated and reproducible workflow. It is the core of MLOps.
CIncorrect
Amazon SageMaker Data Wrangler
Data Wrangler is a tool that performs data preprocessing with no code.
It is not a pipeline feature that automates the entire workflow, so it is incorrect.
DIncorrect
Amazon SageMaker JumpStart
JumpStart is a model hub for using pretrained models right away.
It is not a pipeline feature that automates the entire workflow, so it is incorrect.
Key Takeaway
Remember the correct answer, 'SageMaker Pipelines'.
・It defines and runs steps such as data preparation, training, evaluation, and deployment as an automated and reproducible workflow.
・Because the steps can be run the same way every time, it is the core of MLOps.
Feature Store (storing features), Data Wrangler (preprocessing), and JumpStart (model hub) are not pipelines that automate the entire workflow; they each relate to individual steps or resources.