MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning team is using Amazon SageMaker Experiments to track multiple training runs. They need to compare the performance of different models based on metrics like accuracy and F1 score. However, when they view the experiment list in SageMaker Studio, the metrics are not displayed. What is the MOST likely cause?
⚠ Common exam trap
Many candidates confuse CloudWatch metric definitions (used for hyperparameter tuning or console monitoring) with SageMaker Experiments metric logging, assuming that defining metrics in the algorithm specification is sufficient for Experiments to display them.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The training script did not use the SageMaker SDK to log the metrics.
SageMaker Experiments automatically tracks parameters and metrics only when the training script explicitly logs them using the SageMaker SDK's `log_metric` function or the `sagemaker.experiments.run.Run` class. Without these SDK calls, the metrics are never recorded in the experiment's trial components, so they will not appear in the Studio experiment list. The team must instrument their training code to emit metrics for Experiments to capture them.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The training job did not define metric definitions in the algorithm specification.
Why it's wrong here
Metric definitions are for CloudWatch; Experiments require logging via SDK.
- ✓
The training script did not use the SageMaker SDK to log the metrics.
Why this is correct
Metrics must be logged using experiment.log_metric() or automatically if using frameworks with SageMaker integration.
- ✗
The training job is running on an instance type that does not support Experiments.
Why it's wrong here
All SageMaker instance types support Experiments.
- ✗
The IAM role used by SageMaker does not have permission to write to the Experiments table.
Why it's wrong here
Permissions affect writing, but the issue is that metrics are not displayed, likely not logged.
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