Amazon MLA-C01 Exam - Topic 2 Question 13 Discussion

Actual exam question for Amazon's MLA-C01 exam

Question #: 13
Topic #: 2

A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.

What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

AAdjust the model's parameters and hyperparameters.

BInitiate a manual Model Monitor job that uses the most recent production data.

CCreate a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.

DInclude additional data in the existing training set for the model. Retrain and redeploy the model.

Show Suggested Answer

Suggested Answer: C

by Jill at Nov 23, 2025, 05:47 AM

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Rodney

5 hours ago

Totally agree with C, gotta update the baseline.

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Royal

5 days ago

I think option C makes the most sense. New baseline needed!

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Darrel

11 days ago

C) is the way to go, no doubt. Gotta keep that model monitoring on point, am I right?

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Katie

16 days ago

Haha, A) is like trying to fix a flat tire by adjusting the car's suspension. Nice try, but not the right solution!

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Lisha

21 days ago

Hmm, I'd go with B. Manually running the model monitor job could give you more insights into the data issues.

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Gail

26 days ago

D) sounds like a lot of work. Why not just go with C and save some time?

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Mari

1 month ago

I agree, C is the way to go. Gotta keep that data quality on point!

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Julie

1 month ago

C) is the correct answer. Updating the baseline with the latest dataset is the best way to address the data quality issues identified by Model Monitor.

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Dominque

1 month ago

I practiced a similar question where updating the baseline was key. It makes sense to create a new baseline with the latest data for accurate evaluations.

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Fletcher

2 months ago

I'm leaning towards option C. Updating the baseline and the Model Monitor checks seems like the most prudent way to get a handle on the data quality problems before making any major changes to the model itself.

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Twila

2 months ago

Adding more data to the existing training set and retraining the model could work, but I'd want to be really careful about that. We don't want to just throw more data at the problem without addressing the underlying data quality issues first.

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Rikki

2 months ago

I think creating a new baseline from the latest dataset and updating the Model Monitor to use that new baseline is probably the safest approach here. That way, we can make sure the monitoring is based on the most up-to-date information.

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Franchesca

2 months ago

I feel like initiating a manual Model Monitor job could help, but it seems more like a temporary fix rather than addressing the root cause.

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Jarvis

2 months ago

I think option C is the best choice. New baseline is crucial.

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Tiera

2 months ago

I remember we discussed the importance of creating a new baseline when data quality issues arise. That might be the right approach here.

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Helene

3 months ago

I'm not entirely sure, but I think just adjusting parameters won't really solve the underlying data issues.

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Glory

3 months ago

Okay, so it sounds like we need to investigate the data quality issues more closely. Initiating a manual Model Monitor job with the latest production data seems like a good first step to get a better understanding of what's going on.

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Madalyn

3 months ago

I'm not sure if I should adjust the model parameters or hyperparameters right away. That seems like it could be a bit risky without understanding the root cause of the data quality issues first.

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Tu

3 months ago

I agree, tweaking parameters might not be the best first step.

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