Amazon MLS-C01 Exam - Topic 2 Question 117 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 117
Topic #: 2

[Modeling]

A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadat

a. The solution also must be able to set the data sensitivity and authorship of features.

Which solution will meet these requirements with the LEAST development effort?

AUse Amazon SageMaker Feature Store to select the features. Create a data flow to perform feature-level metadata analysis. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.

BUse Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use SageMaker Studio to analyze the metadata.

CUse Amazon SageMaker Features Store to apply custom algorithms to analyze the feature-level metadata that the company requires. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.

DUse Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use Amazon QuickSight to analyze the metadata.

Show Suggested Answer

Suggested Answer: D

by Santos at Jun 11, 2025, 05:16 AM

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Sharen

2 months ago

Why not just use A? Seems like it covers everything!

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Elbert

3 months ago

Wait, can SageMaker really handle all that?

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Dortha

3 months ago

Option B seems like the simplest choice.

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Ricki

3 months ago

I think D might be better for analysis.

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Scarlet

3 months ago

I agree, B looks efficient for metadata handling!

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Ocie

3 months ago

I vaguely recall that using SageMaker Studio for analysis is a good practice, so maybe B or D is the way to go, but I can't remember the differences clearly.

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Filiberto

4 months ago

I feel like option C could be overkill with custom algorithms. We just need basic metadata analysis, right?

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Elsa

4 months ago

I think option A might be too complex since it involves DynamoDB and QuickSight, which we didn't focus on as much in our practice questions.

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Audra

4 months ago

I remember we discussed the importance of using SageMaker Feature Store for managing features, but I'm not sure if I should choose option B or D.

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Quinn

4 months ago

This is a good opportunity to showcase my knowledge of AWS services. I'll carefully evaluate each option and select the one that most efficiently meets the requirements.

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Fletcher

4 months ago

I'm not too familiar with SageMaker Feature Store, so I'll need to do a quick refresh on how that service works. But I think I can figure out the best approach here.

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Aliza

5 months ago

Option B seems like the most direct solution - using SageMaker Feature Store to manage the feature metadata, and then using SageMaker Studio to analyze it. That seems like the least development effort.

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Earlean

5 months ago

Hmm, I'm a bit confused by all the different AWS services mentioned. I'll need to carefully read through the options and think about how each one addresses the requirements.

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Amie

5 months ago

This looks like a straightforward question about using AWS services to manage feature metadata for machine learning models. I think I can tackle this one.

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Annice

8 months ago

I wonder if the exam writer is trying to trick us with all these options. At the end of the day, the goal is to get the job done with the least effort, right? No need to reinvent the wheel.

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Jennifer

8 months ago

Option A is just overkill. Creating a whole data flow and DynamoDB table? Seems like a lot of extra effort when SageMaker Feature Store can handle this.

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Willis

7 months ago

Yeah, Option D also looks good. Setting feature groups and using QuickSight for metadata analysis.

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King

7 months ago

I agree, Option B seems like a more efficient choice. Setting feature groups and analyzing metadata in SageMaker Studio.

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Erick

8 months ago

Option A is definitely too much. SageMaker Feature Store can handle this without all the extra steps.

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Nada

8 months ago

I'm not sure why anyone would choose option C. Applying custom algorithms to analyze feature metadata? That sounds like way more work than necessary.

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Doug

7 months ago

A: Definitely, option C does seem like it would require a lot more development effort than needed.

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Cristen

8 months ago

B: I agree, option B is the most efficient way to meet the requirements without unnecessary custom algorithms.

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Brittni

8 months ago

A: Option B seems like the best choice. It allows you to set feature groups and analyze metadata using SageMaker Studio.

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Valentin

8 months ago

But option A already includes data sensitivity and authorship settings, which meets the requirements with the least development effort.

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Clemencia

8 months ago

Option D might be the way to go. It seems to cover the key requirements with the least development effort. Plus, QuickSight is a great tool for analyzing the metadata.

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Wendell

8 months ago

I agree, using QuickSight for analyzing the metadata is a smart move.

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Melissa

8 months ago

Option D might be the best choice. It covers the key requirements with minimal effort.

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Elliott

8 months ago

I disagree, I believe option B is more efficient.

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Valentin

9 months ago

I think option A is the best choice.

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Zona

9 months ago

Option B looks like the most straightforward solution to meet the requirements. Using SageMaker Feature Store to manage the feature metadata seems like the way to go.

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