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Question 286 - MLS-C01 discussion

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A music streaming company is building a pipeline to extract features. The company wants to store the features for offline model training and online inference. The company wants to track feature history and to give the company's data science teams access to the features.

Which solution will meet these requirements with the MOST operational efficiency?

A.

Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for online inference. Create an offline store for model training. Create an 1AM role for data scientists to access and search through feature groups.

Answers
A.

Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for online inference. Create an offline store for model training. Create an 1AM role for data scientists to access and search through feature groups.

B.

Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for both online inference and model training. Create an 1AM role for data scientists to access and search through feature groups.

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B.

Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for both online inference and model training. Create an 1AM role for data scientists to access and search through feature groups.

C.

Create one Amazon S3 bucket to store online inference features. Create a second S3 bucket to store offline model training features. Turn on

Answers
C.

Create one Amazon S3 bucket to store online inference features. Create a second S3 bucket to store offline model training features. Turn on

D.

Create two separate Amazon DynamoDB tables to store online inference features and offline model training features. Use time-based versioning on both tables. Query the DynamoDB table for online inference. Move the data from DynamoDB to Amazon S3 when a new SageMaker training job is launched. Create an 1AM policy that allows data scientists to access both tables.

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D.

Create two separate Amazon DynamoDB tables to store online inference features and offline model training features. Use time-based versioning on both tables. Query the DynamoDB table for online inference. Move the data from DynamoDB to Amazon S3 when a new SageMaker training job is launched. Create an 1AM policy that allows data scientists to access both tables.

Suggested answer: A

Explanation:

Amazon SageMaker Feature Store is a fully managed, purpose-built repository for storing, updating, and sharing machine learning features. It supports both online and offline stores for features, allowing real-time access for online inference and batch access for offline model training. It also tracks feature history, making it easier for data scientists to work with and access relevant feature sets.

This solution provides the necessary storage and access capabilities with high operational efficiency by managing feature history and enabling controlled access through IAM roles, making it a comprehensive choice for the company's requirements.


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asked 31/10/2024
Jaimie Lloyd
39 questions
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