MLA-C01 · Question #177
MLA-C01 Question #177: Real Exam Question with Answer & Explanation
The correct answer is A: Configure SageMaker Model Monitor. Establish a data quality baseline. Ensure that the. SageMaker Model Monitor with a data quality baseline is the managed way to detect feature distribution changes by comparing incoming inference data against baseline constraints. Enabling metric emission allows the monitored statistics to be published to CloudWatch so alarms can a
Question
A company has an ML model in Amazon SageMaker AI. An ML engineer needs to implement a monitoring solution to automatically detect changes in the input data distribution of model features. Which solution will meet this requirement with the LEAST operational overhead?
Options
- AConfigure SageMaker Model Monitor. Establish a data quality baseline. Ensure that the
- BConfigure SageMaker Model Monitor. Establish a model quality baseline. Ensure that the
- CUse SageMaker Debugger with custom rules to track shifts in feature distributions. Configure
- DUse Amazon CloudWatch to directly observe the SageMaker AI endpoint's performance metrics.
Explanation
SageMaker Model Monitor with a data quality baseline is the managed way to detect feature distribution changes by comparing incoming inference data against baseline constraints. Enabling metric emission allows the monitored statistics to be published to CloudWatch so alarms can automatically notify when drift-related data quality metrics change, with minimal custom
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