easyMultiple ChoiceObjective-mapped
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 with recent data may incorporate the drifted distribution, but if the drift is due to a data pipeline error, retraining will not fix the underlying issue and could amplify errors.
- ✓
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 first step should be to confirm that the data pipeline is not introducing errors. If the data is correct, then retraining might be appropriate.
- ✗
Rebuild the SageMaker endpoint with a different instance type to improve performance
Why it's wrong here
The endpoint instance type affects latency and throughput, not model accuracy. This will not address data drift.
- ✗
Reduce the number of features in the model by removing 'product_category'
Why it's wrong here
Removing an important feature without understanding the drift could degrade model performance further.
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Senior Network & Security Engineer · founder of Courseiva
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