MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A data scientist notices that a production model's accuracy has degraded over the past week. The training data distribution remains unchanged, but the relationship between features and the target has shifted. Which type of drift is occurring, and which monitoring approach should be used?
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
✓
Concept drift; use SageMaker Model Monitor model quality monitoring with ground truth labels
Concept drift occurs when the underlying relationship between features and target changes. Model quality monitoring (comparing predictions against ground truth) detects this. Data drift monitors feature distribution changes, which are not present here.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bias drift; use SageMaker Clarify post-deployment bias monitoring
Why it's wrong here
Bias drift is about fairness metrics, not accuracy degradation.
- ✗
Data drift; use SageMaker Model Monitor data quality monitoring
Why it's wrong here
Data drift monitors feature distribution. The scenario describes a change in the prediction function, not the input distribution.
- ✗
Feature attribution drift; use SageMaker Clarify
Why it's wrong here
Feature attribution drift monitors SHAP values, not the prediction relationship.
- ✓
Concept drift; use SageMaker Model Monitor model quality monitoring with ground truth labels
Why this is correct
Model quality monitoring compares predictions to actual labels, catching concept drift.
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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.