MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker Experiments to track multiple training runs for a PyTorch model. They want to compare metrics across runs and identify the best hyperparameters. Which TWO capabilities should they use? (Choose TWO.)
⚠ Common exam trap
MLA-C01 often tests the confusion between experiment tracking (SageMaker Experiments) and automated model building or monitoring services (Autopilot, Clarify, Model Monitor), so candidates must map each service to its exact purpose.
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
✓
SageMaker Experiments list and search API to query runs by metric
Option A is correct because the SageMaker Experiments list and search API (e.g., search() with filters on metric values) lets the data scientist query and compare runs across an experiment by metric, which is exactly what is needed to rank runs and identify the best hyperparameters. Option B is correct because the SageMaker SDK's experiment logging capabilities (Run.log_metric, log_parameter, and log_artifact) record the metrics and hyperparameters for each training run so they can later be analyzed and compared. Option C (SageMaker Autopilot) is an automated machine learning service that builds and tunes models automatically, not a tool for comparing metrics across existing runs. Option D (SageMaker Clarify) provides bias detection and explainability, and Option E (SageMaker Model Monitor) detects drift in deployed models; neither supports run-to-run metric comparison or hyperparameter selection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
SageMaker Experiments list and search API to query runs by metric
Why this is correct
The Experiments list and search API lets you query runs programmatically and filter or sort them by logged metric values, so you can retrieve the top-performing runs and compare their hyperparameter configurations. This directly supports identifying the best hyperparameters across many training runs.
- ✓
SageMaker SDK's experiment logging capabilities
Why this is correct
The SageMaker SDK logging capabilities record metrics, parameters and artefacts against each run during training, so every experiment trial is captured consistently. Those logged values then become the data compared across runs to identify which hyperparameters produced the best results.
- ✗
SageMaker Autopilot
Why it's wrong here
Autopilot automates algorithm selection and hyperparameter tuning to build a model, but it does not compare metrics across existing runs or surface the best hyperparameters from tracked experiments. It is tempting because tuning is the goal, and Autopilot would be correct when starting from raw tabular data without a chosen algorithm.
- ✗
SageMaker Clarify
Why it's wrong here
Clarify detects bias and explains predictions; it does not compare metrics across training runs or identify best hyperparameters. It is tempting because it operates on models and metrics, and would be correct for assessing fairness and feature attribution on a completed model, not for experiment run comparison.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects data drift and quality issues on deployed endpoints; it does not compare training-run metrics or identify best hyperparameters. It is tempting because it also analyses model behaviour, and would be correct for ongoing production monitoring of a deployed model rather than experiment comparison during training.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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