MLA-C01 · Question #145
A medical company ingests streams of data from devices that monitor patients' vital signs. The company uses Amazon SageMaker and plans to prepare ML models to predict adverse events for patients. The
The correct answer is D. Use SageMaker Experiments to track the characteristics and results of each iteration.. Amazon SageMaker Experiments is purpose-built for exactly this use case: tracking and comparing hundreds of training runs with different hyperparameters, algorithms, and feature sets. It automatically logs inputs, outputs, parameters, and metrics for each run with minimal code ch
Question
A medical company ingests streams of data from devices that monitor patients’ vital signs. The company uses Amazon SageMaker and plans to prepare ML models to predict adverse events for patients. The dataset is large with thousands of features. An ML engineer needs to run several hundred training iterations with different sets of features, different algorithms, and many potential parameters. The ML engineer must implement a solution to log the characteristics and results of each training iteration. Which solution will meet these requirements with the LEAST implementation effort?
Options
- AUse Amazon CloudWatch to create custom metrics for the characteristics of each iteration.
- BWrite the characteristics of each iteration to logs in Amazon S3. Use AWS Glue and Amazon
- CUse the SageMaker Model Registry to track the characteristics and results of each iteration.
- DUse SageMaker Experiments to track the characteristics and results of each iteration.
How the community answered
(16 responses)- A6% (1)
- C13% (2)
- D81% (13)
Explanation
Amazon SageMaker Experiments is purpose-built for exactly this use case: tracking and comparing hundreds of training runs with different hyperparameters, algorithms, and feature sets. It automatically logs inputs, outputs, parameters, and metrics for each run with minimal code changes-often just a single SDK call. Option A (CloudWatch custom metrics) requires significant manual instrumentation code for every metric. Option B involves building a multi-service pipeline (S3 + Glue + additional tooling), which is high effort. Option C (Model Registry) is designed for versioning and deploying approved models, not for tracking the characteristics of experimental training iterations. SageMaker Experiments provides the lowest implementation effort for the described requirement.
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