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Amazon MLS-C01 Exam - Topic 3 Question 113 Discussion

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.
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
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.

Amazon MLS-C01 Exam - Topic 3 Question 113 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 113
Topic #: 3
[All MLS-C01 Questions]

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?

Show Suggested Answer Hide Answer
Suggested Answer: A

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.


by Maryln at Mar 04, 2025, 07:33 AM

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Brent
6 months ago
Totally agree, A is the way to go for operational efficiency!
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Theron
6 months ago
D seems overly complicated for what they need.
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Jovita
7 months ago
Surprised there's no mention of using a database for this!
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Frank
7 months ago
I think B could work too, but not as well as A.
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Ashton
7 months ago
A is the best choice for tracking feature history efficiently.
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Caprice
7 months ago
I’m leaning towards option D because it mentions time-based versioning, which seems important for tracking feature history, but I’m not confident about the DynamoDB part.
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Raylene
7 months ago
I practiced a similar question where we had to choose between S3 and DynamoDB, but I feel like SageMaker Feature Store is more tailored for this use case.
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Josefa
8 months ago
I'm not entirely sure, but I think option B might be less efficient because it combines both online inference and model training in one store.
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Martin
8 months ago
I remember we discussed the importance of having both online and offline stores for features, so I think option A makes sense for that reason.
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Maryln
8 months ago
I'm leaning towards Option D - using separate DynamoDB tables for online and offline storage, with versioning and S3 integration. That seems like a robust way to handle the history tracking and access requirements. The only thing I'm unsure about is the performance implications of querying DynamoDB for online inference.
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Eladia
8 months ago
Option A looks promising - using SageMaker Feature Store to handle both online and offline storage, with separate stores for each use case. And the IAM role for data scientists is a nice touch to provide controlled access. I think I'll start there and see if I can poke any holes in that solution.
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Kanisha
8 months ago
Hmm, I'm a bit confused by the options. It's not clear to me how the different solutions handle the online and offline storage requirements. I'll need to think through the tradeoffs of each approach more carefully.
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Alishia
8 months ago
This seems like a straightforward question about feature storage and access. I'd start by looking at the key requirements - storing features for offline training and online inference, tracking feature history, and providing data science team access.
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