MLA-C01 ML Model Development Practice Question
A data scientist wants to track the training and validation accuracy of a SageMaker training job over time. They need to visualize these metrics in Amazon CloudWatch. Which action should they take?
⚠ Common exam trap
The trap here is overcomplicating the solution by considering custom code or other SageMaker features, when the built-in metric extraction is sufficient.
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 the SageMaker estimator's metric_definitions parameter to specify regex patterns that extract metrics from the training logs.
The metric_definitions parameter in the SageMaker estimator allows you to define regex patterns that extract metric values from the training logs. SageMaker then automatically publishes these as CloudWatch metrics, enabling visualization. This is the recommended and simplest approach for tracking standard training 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.
- ✗
Configure the training job to write metrics directly to Amazon CloudWatch using the AWS SDK for Python (Boto3) in the training script.
Why it's wrong here
While it is possible to use Boto3 to publish custom metrics to CloudWatch from within the training container, this requires additional code and permissions. The built-in metric_definitions feature is simpler and designed for this purpose. Using Boto3 adds overhead and potential failure points, such as missing IAM permissions.
- ✓
Use the SageMaker estimator's metric_definitions parameter to specify regex patterns that extract metrics from the training logs.
Why this is correct
SageMaker automatically streams training logs to CloudWatch Logs. By defining metric_definitions with regex patterns, SageMaker extracts the specified metrics from the logs and publishes them as CloudWatch metrics. This enables real-time monitoring and visualization without custom code.
- ✗
Write the metrics to a file in the output S3 bucket and create a CloudWatch dashboard from the file.
Why it's wrong here
CloudWatch does not automatically read files from S3 to create metrics. While you can store metrics in S3, you would need a custom process to parse and publish them to CloudWatch. This adds unnecessary complexity and latency. The standard method is to emit metrics directly from the training script.
- ✗
Enable SageMaker Debugger and configure rules to emit metrics to CloudWatch.
Why it's wrong here
SageMaker Debugger is used for debugging model training, such as detecting vanishing gradients or overfitting. It does not automatically publish custom training metrics like accuracy to CloudWatch. While Debugger can collect tensors, it is not the primary mechanism for tracking scalar metrics for dashboards.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.