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MLA-C01 Practice Question: A company trains a model daily using Amazon…
A company trains a model daily using Amazon SageMaker and uses the model for real-time inference. They want to detect data drift between the training data and the inference data to decide when to retrain. Which AWS service should they use for this purpose?
⚠ Common exam trap
Many candidates confuse AWS Glue's data cataloging and ETL capabilities with drift detection, or assume Athena's querying ability can be used for monitoring, but neither service is designed for continuous statistical comparison of ML inference data against training baselines.
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
✓
Amazon SageMaker Model Monitor
Amazon SageMaker Model Monitor is the correct service because it is specifically designed to continuously monitor machine learning models in production for data drift, feature attribution drift, and quality issues. It compares the distribution of live inference data against the baseline training data statistics and alerts when drift exceeds defined thresholds, enabling timely retraining decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon Athena
Why it's wrong here
Amazon Athena is a query service for data in S3, not for drift monitoring.
- ✓
Amazon SageMaker Model Monitor
Why this is correct
SageMaker Model Monitor is designed to detect data drift and model quality degradation.
- ✗
AWS Glue
Why it's wrong here
AWS Glue is used for ETL jobs, not for monitoring data drift.
- ✗
AWS Lambda
Why it's wrong here
AWS Lambda is for serverless compute, not drift detection.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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