- A
Configure the Feature Store to use point-in-time lookup using the training timestamp
Point-in-time lookup ensures that the same feature values used during training are used during serving.
- B
Retrain the model more frequently to adapt to the new feature distributions
Why wrong: This addresses the impact but not the root cause of skew.
- C
Use batch prediction instead of online prediction to ensure consistent features
Why wrong: Batch prediction also uses current features at prediction time, not training time.
- D
Ensure that the training and prediction environments use identical compute resources
Why wrong: Compute resources do not affect feature values.
Question 321 of 1,000
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Point-in-time lookup in Vertex AI Feature Store works by storing feature values with associated timestamps and allowing queries to specify a timestamp parameter. During training, the training timestamp is recorded for each sample; during prediction, the same timestamp is used to retrieve the feature value that was current at that moment. This is analogous to time-travel queries in BigQuery or temporal tables in SQL, ensuring feature consistency across the ML lifecycle.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure the Feature Store to use point-in-time lookup using the training timestamp — Option A is correct because 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.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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