Quick Answer
The answer is to deploy to a Vertex AI Endpoint with multiple replicas and auto-scaling. This configuration is correct because high availability deployment with Vertex AI endpoint replicas ensures redundancy by running the model across several instances, so if one replica fails, traffic is seamlessly routed to the remaining healthy replicas. Auto-scaling dynamically adjusts the number of replicas based on incoming request volume, preventing downtime during traffic spikes while optimizing cost. On the Google Professional Data Engineer exam, this scenario tests your understanding of production-grade serving architecture, often contrasting it with a single-replica deployment or batch prediction, which are common traps for high-availability requirements. A key memory tip: think “replicas for redundancy, auto-scaling for resilience”—without both, you lack true fault tolerance.
This PDE practice question tests your understanding of operationalizing machine learning models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 team uses Vertex AI AutoML Tables to train a model. They need to deploy the model for real-time predictions with high availability. Which deployment configuration should they use?
Export as a Cloud Function
Why wrong: AutoML models are not deployable as Cloud Functions.
Deploy to a Vertex AI Endpoint with 1 replica
Why wrong: No redundancy.
Use a Vertex AI Batch Prediction job
Why wrong: Not real-time.
Deploy to a Vertex AI Endpoint with multiple replicas and auto-scaling
Multiple replicas provide HA.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Deploy to a Vertex AI Endpoint with multiple replicas and auto-scaling
For real-time predictions with high availability, you need a deployment that can handle traffic spikes and failover. Deploying to a Vertex AI Endpoint with multiple replicas and auto-scaling ensures that the model is served from multiple instances, providing redundancy and the ability to scale up or down based on demand. This configuration meets the high-availability requirement by distributing load and automatically recovering from instance failures.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Export as a Cloud Function
Why it's wrong here
AutoML models are not deployable as Cloud Functions.
Deploy to a Vertex AI Endpoint with 1 replica
Why it's wrong here
No redundancy.
Use a Vertex AI Batch Prediction job
Why it's wrong here
Not real-time.
Deploy to a Vertex AI Endpoint with multiple replicas and auto-scaling
Why this is correct
Multiple replicas provide HA.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse batch prediction with real-time serving, or assume that a single replica is sufficient for high availability, not realizing that high availability requires redundancy and automatic scaling.
Detailed technical explanation
Vertex AI Endpoints use a managed prediction service that leverages Kubernetes-based infrastructure under the hood. When you configure multiple replicas with auto-scaling, the endpoint uses a load balancer to distribute incoming requests across healthy replicas, and the auto-scaler adjusts the replica count based on metrics like CPU utilization or request latency. In a real-world scenario, if a model experiences a sudden spike in traffic (e.g., from a marketing campaign), auto-scaling prevents request timeouts by dynamically adding replicas, while multiple replicas ensure that a single zone failure does not cause downtime.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
The correct answer is: Deploy to a Vertex AI Endpoint with multiple replicas and auto-scaling — For real-time predictions with high availability, you need a deployment that can handle traffic spikes and failover. Deploying to a Vertex AI Endpoint with multiple replicas and auto-scaling ensures that the model is served from multiple instances, providing redundancy and the ability to scale up or down based on demand. This configuration meets the high-availability requirement by distributing load and automatically recovering from instance failures.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This PDE 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 PDE exam.
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