MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning team deploys a fraud detection model on a SageMaker endpoint. The model's predictions are used in real-time. The team wants to monitor for data drift by comparing incoming data distributions against a baseline created from the training data. Which SageMaker capability should they use?
⚠ Common exam trap
Candidates often confuse 'data drift' (input distribution changes) with 'model quality drift' (prediction performance changes), leading them to select Model Quality Monitor instead of Data Quality Monitor.
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 Model Monitor - Data Quality Monitor
SageMaker Model Monitor's Data Quality Monitor is specifically designed to detect data drift by comparing the statistical distribution of incoming inference data against a baseline computed from the training dataset. This capability tracks metrics like mean, variance, and quantiles for each feature, alerting when significant deviations occur. For a fraud detection model requiring real-time monitoring of input distributions, this is the correct choice.
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 Model Monitor - Model Quality Monitor
Why it's wrong here
Model Quality Monitor compares predictions against ground-truth labels to detect accuracy degradation, not shifts in input feature distributions. It is tempting because it is the monitoring type that tracks model performance over time, and it would be correct when labelled outcomes arrive with delay and the team wants to alert on declining precision, recall or F1.
- ✓
SageMaker Model Monitor - Data Quality Monitor
Why this is correct
Data Quality Monitor compares incoming request distributions against a baseline computed from training data, detecting drift in feature values. This directly satisfies the requirement to monitor real-time endpoint traffic for data drift, unlike Model Quality Monitor, which tracks prediction accuracy against ground truth labels.
- ✗
SageMaker Model Monitor - Feature Attribution Drift Monitor
Why it's wrong here
Feature Attribution Drift Monitor tracks changes in the relative influence of input features on predictions, using explainability baselines rather than raw data distributions. It suits detecting concept drift in model reasoning, not comparing incoming feature distributions against training data. Data Quality Monitor, with its statistical baselines, satisfies the stated requirement.
- ✗
SageMaker Model Monitor - Bias Drift Monitor
Why it's wrong here
Bias Drift Monitor compares predicted labels against ground truth to detect bias, not input feature distributions against a training baseline. It is tempting because it is a Model Monitor type, but Data Drift Monitor is the capability that compares incoming data distributions with the baseline.
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JA
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.