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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
Athena queries data held in Amazon S3 using SQL, but it neither captures live inference traffic nor computes drift statistics between training and inference distributions. It is tempting because Athena is the natural choice for ad-hoc analytical queries over large datasets already stored in S3.
- ✓
Amazon SageMaker Model Monitor
Why this is correct
SageMaker Model Monitor compares live inference data against the training baseline and raises CloudWatch alerts when drift exceeds thresholds. That directly satisfies the requirement to detect data drift between training and inference data and decide when to retrain.
- ✗
AWS Glue
Why it's wrong here
Glue performs ETL and catalogue operations, moving and transforming data, but it does not compare live inference inputs against training baselines or emit drift metrics. It is tempting because Glue is the standard service for preparing and transforming datasets that feed SageMaker training jobs.
- ✗
AWS Lambda
Why it's wrong here
Lambda runs event-driven code but provides no built-in drift detection algorithms, baselines, or monitoring against training data distributions. It is tempting because Lambda is the usual glue for lightweight automation, such as triggering a retraining pipeline when a custom drift check fires.
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Senior Network & Security Engineer · founder of Courseiva
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