Quick Answer
The answer is to deploy the model to a Vertex AI Endpoint with automatic scaling. This option is correct because Vertex AI Endpoints are purpose-built for online prediction with minimal latency, and when you enable automatic scaling, the service dynamically adjusts the number of replicas based on incoming traffic load, handling spikes without any manual intervention. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of deployment strategies for real-time inference, often contrasting Vertex AI Endpoints with batch prediction or custom serving containers. A common trap is choosing manual scaling for predictability, but that fails to address the requirement for handling traffic spikes automatically. Remember the key distinction: for real-time, elastic serving, always pair an Endpoint with autoscaling. A useful memory tip is “Endpoint + Autoscale = Spike-proof real-time inference.”
This PMLE practice question tests your understanding of serving and scaling models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 data science team has trained a TensorFlow model and wants to serve it online with minimal latency. Which Vertex AI deployment option should they use to ensure the model can handle traffic spikes without manual scaling?
Use Vertex AI Model Garden.
Why wrong: Provides pre-trained models, not a deployment service.
Deploy the model to a Vertex AI Endpoint with automatic scaling.
Autoscaling handles traffic spikes with low latency.
Use Vertex AI Batch Prediction for offline inference.
Why wrong: Designed for batch, not low-latency online serving.
Deploy the model to a Compute Engine VM with a load balancer.
Why wrong: Manual scaling and management overhead.
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 the model to a Vertex AI Endpoint with automatic scaling.
Vertex AI Endpoints with automatic scaling (option B) are designed for online serving with minimal latency and can automatically adjust the number of replicas based on traffic load, handling spikes without manual intervention. This is the correct choice for a TensorFlow model requiring real-time inference and elastic scaling.
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.
Use Vertex AI Model Garden.
Why it's wrong here
Provides pre-trained models, not a deployment service.
Deploy the model to a Vertex AI Endpoint with automatic scaling.
Why this is correct
Autoscaling handles traffic spikes with low latency.
Related concept
Read the scenario before looking for a memorised answer.
Use Vertex AI Batch Prediction for offline inference.
Why it's wrong here
Designed for batch, not low-latency online serving.
Deploy the model to a Compute Engine VM with a load balancer.
Why it's wrong here
Manual scaling and management overhead.
Common exam traps
Google Cloud often tests the misconception that any cloud deployment with a load balancer (like Compute Engine) provides automatic scaling, but the trap here is that Vertex AI Endpoints offer managed autoscaling natively, whereas Compute Engine VMs require additional infrastructure setup and do not automatically scale without configuring managed instance groups.
Detailed technical explanation
Vertex AI Endpoints use a horizontal pod autoscaler that adjusts the number of prediction nodes based on CPU utilization or request throughput, with a target utilization set via the `min_replica_count` and `max_replica_count` parameters. Under the hood, the endpoint leverages a gRPC or REST API with an optimized TensorFlow Serving container, enabling sub-100ms latency for typical models. In a real-world scenario, if traffic spikes from 10 to 10,000 requests per second, the autoscaler can provision additional replicas within minutes, but cold start latency may occur if scaling from zero; setting a `min_replica_count` of 1 avoids this.
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
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
The correct answer is: Deploy the model to a Vertex AI Endpoint with automatic scaling. — Vertex AI Endpoints with automatic scaling (option B) are designed for online serving with minimal latency and can automatically adjust the number of replicas based on traffic load, handling spikes without manual intervention. This is the correct choice for a TensorFlow model requiring real-time inference and elastic scaling.
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 30, 2026
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.
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