Amazon Exam MLA-C01 Topic 4 Question 8 Discussion

Actual exam question for Amazon's MLA-C01 exam

Question #: 8
Topic #: 4

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 Kristel at Jul 10, 2025, 09:00 PM

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Lezlie

11 hours ago

B is an interesting choice, but I'm not sure a manual Model Monitor job would be enough to fix the underlying data quality issues.

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Nichelle

3 days ago

D sounds like a good option too. Expanding the training set and retraining the model could help resolve the data quality problems.

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Paris

3 days ago

I think we should adjust the model's parameters and hyperparameters.

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Chandra

5 days ago

Hmm, I think C is the way to go. Updating the baseline with the latest data seems like the logical step to address the data quality issues.

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