MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker Experiments to track multiple training runs. They want to compare runs based on the objective metric and visualize performance. Which THREE steps should they perform? (Choose THREE.)
⚠ Common exam trap
MLA-C01 often tests whether candidates conflate Experiment tracking with deployment or monitoring, causing them to select Model Monitor or endpoint deployment as part of the comparison workflow.
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
✓
Use SageMaker Studio Experiments UI to list and compare trials
Option D is correct because a SageMaker Experiment is the top-level container that groups related trials (training runs) so they can be tracked and compared together. Option C is correct because the SageMaker SDK (e.g., Run, Trial, Tracker, or the log_parameters/log_metric calls) is how hyperparameters and objective metrics get recorded for each run, which is required before any comparison can be made. Option B is correct because the SageMaker Studio Experiments UI lets the data scientist list trials and compare them visually by objective metric, directly satisfying the stated goal of comparing runs and visualizing performance. Option A is not required because deploying the best model to an endpoint is an inference/hosting step, not part of tracking or comparing experiments. Option E is not required because Model Monitor is used for detecting drift and data quality issues on deployed endpoints, not for logging or comparing training-run metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the best model to an endpoint
Why it's wrong here
Deploying a model to an endpoint serves inference hosting, not run comparison or visualisation within SageMaker Experiments. It is tempting because Experiments can identify a best run, and endpoint deployment would be correct if the goal were serving the chosen model to applications.
- ✓
Use SageMaker Studio Experiments UI to list and compare trials
Why this is correct
The SageMaker Studio Experiments UI lists trials and plots objective metrics side by side, enabling direct comparison and visualisation of performance across runs. This satisfies the requirement to compare runs by objective metric and visualise their results.
- ✓
Log hyperparameters and metrics using the SageMaker SDK
Why this is correct
Logging hyperparameters and metrics through the SageMaker SDK writes each run's parameters and objective metric into the experiment trial component, which is the prerequisite for comparison and visualisation. Without this recorded data, SageMaker Experiments has nothing to plot or rank, so the metric-based comparison the scenario requires cannot occur.
- ✓
Create a SageMaker Experiment
Why this is correct
Creating a SageMaker Experiment establishes the logical container that groups related training runs, which is the prerequisite for tracking and comparing them. Without this parent entity, runs cannot be associated or queried together, so the objective-metric comparison and visualisation the stem requires become impossible.
- ✗
Enable SageMaker Model Monitor for each run
Why it's wrong here
Model Monitor detects data drift and quality issues on deployed endpoints; it neither compares experiment runs nor visualises metrics. It is tempting because Experiments and Monitor both sit in the SageMaker ML lifecycle, and Monitor would be correct if the requirement were ongoing production drift detection.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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