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Amazon MLA-C01 Exam - Topic 2 Question 22 Discussion

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?
C) Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.
A) Adjust the model's parameters and hyperparameters.
B) Initiate a manual Model Monitor job that uses the most recent production data.
D) Include additional data in the existing training set for the model. Retrain and redeploy the model.

Amazon MLA-C01 Exam - Topic 2 Question 22 Discussion

Actual exam question for Amazon's MLA-C01 exam
Question #: 22
Topic #: 2
[All MLA-C01 Questions]

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?

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Suggested Answer: C

When Model Monitor identifies data quality issues, it might be due to a shift in the data distribution compared to the original baseline. By creating a new baseline using the most recent production data and updating Model Monitor to evaluate against this baseline, the ML engineer ensures that the monitoring is aligned with the current data patterns. This approach mitigates false positives and reflects the updated data characteristics without immediately retraining the model.


by King at Jun 28, 2026, 02:54 AM

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Leatha
1 day ago
Adding more data (D) could help, but it depends on the quality of that data.
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Malcolm
6 days ago
Wait, can you really just update the baseline like that? Sounds too easy!
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Lezlie
12 days ago
Adjusting parameters won't fix data quality issues, right?
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Winifred
17 days ago
Disagree, I’d go with B. Manual checks are super important.
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Bobbye
22 days ago
I think option C makes the most sense. New baseline is key!
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Valentine
27 days ago
I practiced a similar question where updating the baseline was the right answer, so I’m leaning towards option C here.
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Clement
1 month ago
I feel like adjusting parameters could be a quick fix, but it doesn't really address the data quality concerns directly.
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Domingo
1 month ago
I think initiating a manual Model Monitor job is a good way to check the most recent data, but it might not solve the underlying issues.
upvoted 0 times
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Ciara
1 month ago
I remember we discussed how creating a new baseline could help address data quality issues, but I'm not entirely sure if that's the only step needed.
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