MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using Amazon SageMaker to train a model and wants to track experiments, including parameters and metrics. Which THREE actions should be taken? (Choose three.)
⚠ Common exam trap
Candidates often confuse logging to CloudWatch (which is for raw logs) with structured experiment tracking, or assume SageMaker Studio provides a manual recording interface, when in fact all experiment tracking must be done programmatically via the SDK or console API.
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
✓
Create an experiment in SageMaker Experiments.
SageMaker Experiments is the native service for organizing, tracking, and comparing machine learning training runs. It provides a structured way to create experiments, trials, and trial components, enabling systematic tracking of parameters and metrics without manual overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Studio to manually record experiments.
Why it's wrong here
Manual recording is not scalable.
- ✗
Use Amazon CloudWatch Logs to store experiment data.
Why it's wrong here
CloudWatch is not for experiment tracking.
- ✓
Create an experiment in SageMaker Experiments.
Why this is correct
Experiments organize runs.
- ✓
Use the SageMaker SDK to log parameters and metrics in the training script.
Why this is correct
Logging tracks values.
- ✓
Use the SageMaker SDK to create a trial and trial component.
Why this is correct
Trials capture runs.
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