easyMultiple Select
PMLE Practice Question: An ML team is deploying a model to Vertex AI for…
An ML team is deploying a model to Vertex AI for the first time. Which THREE are best practices for scaling from prototype to production?
⚠ Common exam trap
Google Cloud often tests the misconception that manual scaling or single-instance architectures are simpler and more reliable, but the PMLE exam emphasizes automated, resilient, and consistent practices like autoscaling and feature stores for production ML workloads.
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
✓
Store all features in a Feature Store for consistency.
Option B is correct because storing features in a Vertex AI Feature Store ensures the same feature transformations are used at training and serving time, preventing training-serving skew and enabling feature reuse and consistency across models. Option D is correct because production models degrade over time due to data drift and concept drift, so continuous monitoring of prediction quality, accuracy, and drift metrics is essential to detect degradation and trigger remediation. Option E is correct because automating retraining and deployment with Vertex AI Pipelines creates a repeatable, auditable MLOps workflow that reduces manual error and lets the model adapt to new data efficiently. Option A is not a best practice because manual scaling based on historical patterns is reactive and error-prone; production should use autoscaling driven by live metrics such as CPU, GPU, or request load. Option C is not a best practice because a single large instance creates a single point of failure and does not scale horizontally, whereas Vertex AI supports distributed serving with multiple replicas for resilience and elasticity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually scale instances based on historical traffic patterns.
Why it's wrong here
Manual scaling is error-prone; autoscaling is recommended.
- ✓
Store all features in a Feature Store for consistency.
Why this is correct
A Feature Store provides a centralised, versioned repository so training and serving pipelines draw identical feature values, eliminating training-serving skew. This directly satisfies the production-consistency requirement when scaling beyond a prototype, where ad-hoc feature engineering typically diverges between environments.
- ✗
Use a single large instance to simplify management.
Why it's wrong here
Consolidating onto one large instance removes horizontal redundancy, so a single zone or node failure takes the endpoint offline; Vertex AI autoscaling distributes replicas across instances instead. It tempts teams wanting fewer moving parts, and suits batch or experimental workloads where throughput matters but availability does not.
- ✓
Monitor model performance for drift and accuracy degradation.
Why this is correct
Continuous monitoring detects data drift and accuracy degradation in production, where input distributions shift away from training data. This satisfies the production-readiness requirement by triggering alerts or retraining before silent model failure degrades business outcomes.
- ✓
Automate model retraining and deployment using Vertex AI Pipelines.
Why this is correct
Vertex AI Pipelines codifies retraining and deployment as repeatable, orchestrated workflows, removing manual steps that cause inconsistency at scale. This satisfies the automation requirement for production, enabling scheduled retraining and controlled rollout as data and models evolve.
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