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Amazon MLA-C01 Exam - Topic 3 Question 1 Discussion

Actual exam question for Amazon's MLA-C01 exam
Question #: 1
Topic #: 3
[All MLA-C01 Questions]

Case Study

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a

central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application.

Which action will meet this requirement?

Show Suggested Answer Hide Answer
Suggested Answer: A

by Filiberto at Jun 14, 2025, 02:05 AM

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Gracia
3 months ago
D just feels too manual for on-demand workflows.
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Ardella
3 months ago
Wait, can SageMaker Clarify really handle real-time monitoring?
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Catrice
3 months ago
I think B could work too, but not as effective.
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Arleen
3 months ago
C seems like a stretch for this use case.
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Herminia
3 months ago
A is the best choice for monitoring bias drift.
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Erasmo
4 months ago
I vaguely recall that SageMaker notebooks are more for analysis rather than real-time monitoring, so I’m leaning away from option D.
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Jonell
4 months ago
I practiced a similar question where we had to choose between using Lambda and SageMaker tools, and I think invoking a Lambda function makes sense here.
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Andra
4 months ago
I'm not entirely sure, but I feel like using AWS Glue Data Quality might not be the best fit for monitoring bias drift.
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Felix
4 months ago
I remember studying about SageMaker Clarify and its role in monitoring bias, so I think option A could be the right choice.
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Talia
4 months ago
I'm feeling pretty confident about this one. The question is specifically asking for a solution to monitor bias drift, and Option A directly addresses that requirement by using SageMaker Clarify, which is the recommended tool for bias detection.
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Yan
5 months ago
Okay, I think I've got a strategy here. The key is that we need to run an on-demand workflow to monitor bias drift for deployed models. Option A seems to be the best fit, as it mentions invoking a Lambda function to run a SageMaker Clarify job, which is designed for bias detection.
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Ettie
5 months ago
Hmm, I'm a bit confused by the options. I'm not sure how to differentiate between using a Lambda function and the built-in SageMaker image. I'll need to review the details more closely.
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Laticia
5 months ago
This looks like a tricky question. I'll need to carefully read through the requirements and think about the different options.
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Mirta
8 months ago
I'm not sure about option C. AWS Glue Data Quality may not be as effective in monitoring bias drift compared to SageMaker Clarify.
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Vanna
8 months ago
I agree with Walker. Using SageMaker Clarify will ensure that bias drift is monitored effectively in real-time endpoints.
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Teri
8 months ago
Hold on, why are we even considering SageMaker notebooks? That's overkill for just comparing bias. Option D is a bit of a stretch, in my opinion.
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Tony
7 months ago
C) Use AWS Glue Data Quality to monitor bias.
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Shawana
7 months ago
B) Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.
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Elmira
7 months ago
A) Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.
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Walker
8 months ago
I think option A is the best choice because SageMaker Clarify is specifically designed for monitoring bias in ML models.
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Mauricio
8 months ago
I'm not so sure about that. Isn't AWS Glue Data Quality designed for more general data quality monitoring? Option C might be a better fit for this specific use case.
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Anabel
8 months ago
I'm not sure about that. Wouldn't it be easier to just use the built-in SageMaker image for model monitoring? Option B seems more efficient to me.
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Merissa
8 months ago
I agree, using the built-in SageMaker image for model monitoring sounds like the most efficient option.
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Ryan
8 months ago
Option B seems like a good choice. It would definitely make model monitoring easier.
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Lindsey
9 months ago
Option A looks like the way to go here. Invoking a Lambda function to run a SageMaker Clarify job seems like the most straightforward approach to monitoring bias drift.
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Nidia
8 months ago
User 3: It's important to ensure secure and isolated use of training data during the ML lifecycle.
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Danilo
9 months ago
User 2: Definitely, using a Lambda function to run a SageMaker Clarify job is a good solution.
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Valda
9 months ago
User 1: I agree, option A seems like the best choice for monitoring bias drift.
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