hardMultiple ChoiceObjective-mapped
PDE Practice Question: A company uses Vertex AI Feature Store for…
A company uses Vertex AI Feature Store for serving features to both training and prediction. The team notices that predictions made shortly after training use different feature values, causing a training-serving skew. What is the most effective way to prevent this skew?
⚠ Common exam trap
A common pitfall is assuming that retraining more frequently or switching prediction methods can resolve training-serving skew. The root cause is temporal inconsistency in feature values, which requires point-in-time lookups to ensure the same feature values are used during training and prediction.
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
✓
Configure the Feature Store to use point-in-time lookup using the training timestamp
Point-in-time lookup ensures that feature values used during training are exactly the same as those used during prediction by retrieving the feature value as it existed at the training timestamp. This directly addresses training-serving skew caused by time-dependent feature changes, which is a common issue in Vertex AI Feature Store when features are updated after training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure the Feature Store to use point-in-time lookup using the training timestamp
Why this is correct
Point-in-time lookup ensures that the same feature values used during training are used during serving.
- ✗
Retrain the model more frequently to adapt to the new feature distributions
Why it's wrong here
This addresses the impact but not the root cause of skew.
- ✗
Use batch prediction instead of online prediction to ensure consistent features
Why it's wrong here
Batch prediction also uses current features at prediction time, not training time.
- ✗
Ensure that the training and prediction environments use identical compute resources
Why it's wrong here
Compute resources do not affect feature values.
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