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MLA-C02 practice question: Experiments and reproducibility

ML Model and Foundation Model (FM) Development A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A regional bank has three data science teams training credit models in SageMaker AI notebooks. Each team tracks its runs in spreadsheets, so no one can compare parameters, metrics and artifacts across teams or reproduce last quarter's best run.

The bank wants one shared place where every team logs runs and metrics, with a UI to compare them, and it wants the tracking server to be managed by AWS rather than run by the bank.

Which solution meets these requirements?

Choose one answer.

  1. A. Ask each team to save its metrics in a shared Amazon S3 folder as CSV files, named by date and team.
  2. B. Give each team its own Amazon EC2 instance running a self-hosted MLflow server, and have the teams share their URLs by email.
  3. C. Create a managed MLflow tracking server in SageMaker AI, and have each team log runs to it with the MLflow client.
  4. D. Store every trained model in the SageMaker Model Registry only, since the registry tracks metrics for each run.
Show the answer and explanation

Answer: C

  • A. Wrong: Shared files provide storage but no run comparison UI, and they rely on naming discipline for reproducibility. The bank also needs parameters and artifacts linked to each run.
  • B. Wrong: A self-hosted server puts the operation of the tracking server on the bank, which the stem rules out. Sharing by email does not give one place for all runs.
  • C. Right: Correct. SageMaker AI provides managed MLflow tracking servers, which give a shared place to log parameters, metrics and artifacts and to compare runs in a UI. AWS operates the server for the bank. Learn more: Managed MLflow on Amazon SageMaker AI (opens in a new tab)
  • D. Wrong: The Model Registry tracks model versions and approval status, not the parameters and metrics of every experiment run. It does not replace experiment tracking.

Explanation

SageMaker AI managed MLflow tracking servers let teams log runs and compare parameters, metrics and artifacts in one place, with AWS managing the server. Shared files lack a comparison interface, self-hosting adds operations, and the Model Registry tracks versions rather than every experiment.

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