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PMLE Practice Question: Which TWO are best practices when deploying…
Which TWO are best practices when deploying AutoML models to production?
⚠ Common exam trap
Google Cloud often tests the misconception that TPUs are suitable for model serving, but TPUs are optimized for training and not supported for Vertex AI AutoML serving, which uses CPUs or GPUs for inference.
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
✓
Monitor for data drift
Monitoring for data drift (Option A) is a best practice because production models can degrade over time as the statistical properties of input data change. Vertex AI provides a Model Monitoring service that automatically detects skew and drift by comparing serving data distribution against training data distribution, triggering alerts when anomaly thresholds are breached. This ensures model reliability and performance in production.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Monitor for data drift
Why this is correct
Data drift can degrade performance; monitoring is essential.
- ✗
Train the model on a disk to reduce latency
Why it's wrong here
Training is separate from deployment; disk choice not a best practice.
- ✓
Enable Vertex AI Explainability
Why this is correct
Explainability provides insights into predictions.
- ✗
Deploy on sole-tenant nodes
Why it's wrong here
Sole-tenant nodes are for compliance, not a general best practice.
- ✗
Use TPUs for model serving
Why it's wrong here
TPUs are typically used for training, not for serving predictions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.