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
Ignoring the change leaves the degraded accuracy unaddressed; concept drift alters the input-to-target mapping, so retraining on recent labelled data is required. Monitoring input distribution alone suits covariate drift detection, where feature distributions shift but the underlying relationship stays fixed.
- ✗
Data drift; update the baseline statistics and continue monitoring
Why it's wrong here
Updating baseline statistics tracks input distribution changes; here inputs are stable, so the degraded feature-target relationship is concept drift requiring model retraining. This response would be correct if monitoring showed covariate shift in the incoming feature values.
- ✓
Concept drift; retrain the model with newly collected labeled data
Why this is correct
Concept drift occurs when the relationship between input features and the target changes while the input distribution stays stable, exactly as described. Retraining with newly collected labelled data lets the model relearn the updated feature-to-target mapping and restore accuracy.
- ✗
Data drift; retrain the model with the latest training data
Why it's wrong here
The stem describes concept drift: the feature-to-target relationship shifted while inputs stayed stable, so retraining on the same distribution addresses the wrong cause. Data drift monitoring would be correct where input feature distributions themselves diverge from the training baseline.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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