easyMultiple Choice
MLA-C01 Practice Question: A machine learning team at a retail company has…
A machine learning team at a retail company has deployed a product recommendation model using Amazon SageMaker. The model is updated weekly with new data. Recently, the team noticed that the model's accuracy on a holdout evaluation set has been declining over the past month. The data pipeline that feeds the training job has not changed. The team suspects data drift. They have SageMaker Model Monitor enabled on the inference endpoint and have set up Amazon CloudWatch metrics for feature distribution distances. Upon reviewing the CloudWatch dashboards, they see that the feature distribution distance metric for the most important feature 'product_category' has increased significantly. However, the team is unsure if this is the root cause. Which remediation step should the team take FIRST?
⚠ Common exam trap
Many exam-takers assume data drift always requires retraining, but the first remediation step should always be to investigate the data pipeline to rule out upstream errors before taking corrective action on the model.
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
✓
Investigate the data pipeline that feeds the training job to ensure consistent data collection and encoding of the 'product_category' feature
The first step when data drift is suspected is to investigate the data pipeline to ensure consistent data collection and encoding. Since the model's accuracy is declining and the feature distribution distance for 'product_category' has increased, the root cause may be a change in how the feature is collected or encoded upstream, not necessarily a change in the underlying data distribution. SageMaker Model Monitor detects drift in feature distributions, but it cannot diagnose the cause; the team must verify the pipeline before retraining or modifying the model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retrain the model using the most recent week of data and redeploy to the endpoint
Why it's wrong here
Retraining immediately on one week of data treats an unconfirmed cause; the elevated distribution distance only flags drift, so the team must first validate whether 'product_category' drift actually degrades predictions. Retraining is the right remediation once drift is confirmed as the root cause.
- ✓
Investigate the data pipeline that feeds the training job to ensure consistent data collection and encoding of the 'product_category' feature
Why this is correct
The rising distribution distance on product_category points to an input-side change. Before retraining or altering the model, verify the pipeline still collects and encodes that feature consistently, since encoding drift alone can inflate the metric without genuine concept drift.
- ✗
Rebuild the SageMaker endpoint with a different instance type to improve performance
Why it's wrong here
Instance type affects compute capacity, latency and cost, not feature distributions or model accuracy, so it cannot address drift in 'product_category'. It is tempting when endpoint performance degrades, and changing instance type would be correct for throughput, memory or latency problems rather than accuracy loss.
- ✗
Reduce the number of features in the model by removing 'product_category'
Why it's wrong here
Dropping 'product_category' discards the most important predictor without evidence it causes the accuracy decline, and removal itself changes the feature space. It is tempting as a quick drift fix, and feature removal would be correct if that feature were confirmed noisy, leaky or unavailable at inference.
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