MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
An ML team has deployed a model to a SageMaker real-time endpoint and wants to set up automated monitoring for model quality. Which TWO elements are required to configure SageMaker Model Monitor for model quality? (Select TWO.)
⚠ Common exam trap
Test-takers frequently confuse the requirements for model quality monitoring (which needs ground truth labels and captured predictions) with those for data quality monitoring (which needs a baseline statistics file and constraints), leading them to select options B or E incorrectly.
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
✓
A ground truth labels dataset for comparison
SageMaker Model Monitor for model quality requires a ground truth labels dataset to compare the model's predictions against actual outcomes. This comparison is essential for calculating quality metrics like accuracy, precision, recall, or F1 score, which indicate how well the model is performing over time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SHAP values for feature attribution
Why it's wrong here
SHAP values are used for feature attribution drift monitoring, not model quality.
- ✗
A constraints file with allowed deviation thresholds
Why it's wrong here
Constraints file is used for data quality monitoring to define acceptable drift thresholds.
- ✓
A ground truth labels dataset for comparison
Why this is correct
Ground truth labels are essential to compare against predictions and compute model quality metrics.
- ✓
The endpoint's prediction output captured in real-time
Why this is correct
Model Monitor captures predictions from the endpoint to compare against ground truth.
- ✗
A baseline statistics file derived from the training data
Why it's wrong here
Baseline statistics are required for data quality monitoring, not model quality.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.