PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A machine learning team needs to ensure that the same features used for training are used for serving in production to avoid training-serving skew. They use Vertex AI Feature Store. Which THREE actions should they take?
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
✓
Enable point-in-time correct retrieval when creating training datasets
Option A is correct because point-in-time correct retrieval ensures training datasets are built from the exact feature values that were available at the time of each event, preventing label leakage and keeping training data consistent with what would have been served historically. Option C is correct because using the same feature view for both training data export and online serving guarantees that the identical feature transformation and source are used in both paths, which is the core defense against training-serving skew. Option E is correct because feature monitoring detects drift in feature distributions between training and serving, surfacing skew or data quality issues so the team can remediate them. Option B is incorrect because deliberately using different feature views for training and serving introduces skew rather than preventing it. Option D is incorrect because exporting training data directly from the online store is not the recommended pattern; training datasets should come from the offline store with point-in-time correctness, while the online store serves low-latency predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable point-in-time correct retrieval when creating training datasets
Why this is correct
Point-in-time correct retrieval reconstructs the feature values that existed at each training label's timestamp, preventing future data from leaking into training. This directly satisfies the stem's requirement that training features match those available at serving time, eliminating training-serving skew.
- ✗
Use different feature views for training and serving to compare performance
Why it's wrong here
Separate feature views for training and serving reintroduce the skew the team must eliminate, because each view can resolve different transformations and freshness. A single shared feature view is required. Distinct views are legitimate when deliberately serving different models or environments, not when training and inference must draw identical feature values.
- ✓
Use the same feature view for both training data export and online serving
Why this is correct
A single feature view supplies identical transformation logic and feature values to both batch training export and online serving. Sharing this definition removes divergence between the two paths, which is precisely the training-serving skew the team must avoid.
- ✗
Export training data from the online store directly
Why it's wrong here
Exporting training data from the online store is not a supported Vertex AI Feature Store pattern; the online store serves low-latency inference reads, while training reads come from the offline store or a BigQuery source. Online export would be chosen only for small, latency-sensitive retrieval scenarios, not bulk training extraction.
- ✓
Set up feature monitoring to detect drift in feature distributions
Why this is correct
Feature monitoring detects distribution drift between training and serving data, but it only observes divergence after it occurs; it does not enforce that identical feature transformations are applied at both ends. It satisfies the ongoing validation requirement, complementing point-in-time correctness and a shared transformation pipeline, rather than preventing skew itself.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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