MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company deploys a model with SageMaker and wants to monitor for concept drift. They have noticed that the relationship between input features and the target variable has changed, causing model accuracy to degrade. However, the input data distribution remains stable. Which type of drift is this, and what is the most appropriate response strategy?
⚠ Common exam trap
A common mix-up: candidates confuse concept drift with data drift, assuming any drift requires updating baseline statistics, when in fact concept drift demands retraining with fresh labeled data to realign the model with the new feature-target relationship.
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; retrain the model with newly collected labeled data
This is concept drift because the relationship between input features and the target variable has changed while the input data distribution remains stable. The most appropriate response is to retrain the model with newly collected labeled data that reflects the current relationship, as concept drift requires updating the model's learned mapping from features to labels.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Concept drift; ignore the change as long as input distribution remains stable
Why it's wrong here
Concept drift degrades model performance and must be addressed, not ignored.
- ✗
Data drift; update the baseline statistics and continue monitoring
Why it's wrong here
Updating baseline does not address concept drift; the model itself needs retraining.
- ✓
Concept drift; retrain the model with newly collected labeled data
Why this is correct
Concept drift is a change in P(y|x). Retraining with recent labeled data adjusts the model to the new relationship.
- ✗
Data drift; retrain the model with the latest training data
Why it's wrong here
Data drift refers to change in input distribution P(x), which is not the case here.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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