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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 66 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 66
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate 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?

  • A. Adjust the model's parameters and hyperparameters.
  • B. Initiate a manual Model Monitor job that uses the most recent production data.
  • C. Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.
  • D. Include additional data in the existing training set for the model. Retrain and redeploy the model.
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Suggested Answer: C 🗳️
by GiorgioGss at Nov. 28, 2024, 8:40 a.m.
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Certified101
3 weeks, 5 days ago
Selected Answer: C
agree with GiorgioGss - If the problems start appearing "After a model update" then C is the only valid option.
upvoted 1 times
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Ell89
2 months ago
Selected Answer: D
the model needs to be retrained
upvoted 2 times
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Saransundar
3 months ago
Selected Answer: C
Model Monitor gives data quality issues --> Create new baseline --> Validate baseline --> Update Model Monitor with new baseline --> Reevaluate data quality --> Investigate and fix root cause (if issues persist) --> Monitor continuously
upvoted 1 times
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GiorgioGss
3 months ago
Selected Answer: C
If the problems start appearing "After a model update" then C is the only valid option.
upvoted 1 times
...
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