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 concerns changes in model fairness across protected groups, not the feature-target relationship. Clarify post-deployment bias monitoring measures disparate impact, so it would not detect the accuracy loss described. It is tempting because Clarify is a real SageMaker monitoring tool, but the scenario describes concept drift, which requires model quality monitoring.
- ✗
Data drift; use SageMaker Model Monitor data quality monitoring
Why it's wrong here
Data drift means the input feature distribution changes, yet the stem states the training data distribution is unchanged and only the feature-target relationship shifted, which is concept drift. Data quality monitoring tracks feature statistics, so it would miss this. It is tempting because data drift is the commonest drift type, but it does not match this scenario.
- ✗
Feature attribution drift; use SageMaker Clarify
Why it's wrong here
Feature attribution drift measures changes in the relative importance of input features, not the mapping from features to target. Clarify's explainability monitoring would flag shifting attributions, but the stem describes a changed feature-target relationship, which is concept drift. It is tempting because Clarify is a genuine SageMaker monitoring capability, but it addresses a different axis.
- ✓
Concept drift; use SageMaker Model Monitor model quality monitoring with ground truth labels
Why this is correct
Concept drift is a change in the relationship between features and target while input distribution stays stable, so predictions degrade despite unchanged data. Model quality monitoring with ground truth labels detects this by comparing predicted against actual outcomes.
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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.