The answer is to deploy all models to a single Vertex AI endpoint and configure traffic splitting. This approach is correct because Vertex AI endpoints allow you to host multiple models behind one endpoint, where traffic splitting routes a specified percentage of requests to each model, enabling shared compute resources and reducing costs compared to provisioning separate endpoints for each model. On the Google Professional Data Engineer exam, this scenario tests your understanding of Vertex AI’s resource optimization features, often appearing as a trap where candidates mistakenly choose separate endpoints for different latency or memory profiles. The key insight is that traffic splitting decouples model deployment from endpoint infrastructure, allowing you to balance cost and performance without dedicated hardware per model. Remember the mnemonic “One Endpoint, Many Models, Split Traffic” to recall that sharing resources through a single endpoint is the cost-efficient path.
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning 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 company serves multiple models using Vertex AI endpoints. Each model has different latency and memory requirements. To minimize cost, the company wants to share underlying compute resources among models. Which approach should they use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy all models to a single Vertex AI endpoint and configure traffic splitting.
Vertex AI endpoints support traffic splitting, allowing you to deploy multiple models behind a single endpoint and route a percentage of traffic to each model. This enables resource sharing and cost optimization because the underlying compute infrastructure is shared among the models, unlike separate endpoints which would each require dedicated resources.
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.
✗
Deploy each model as a separate Cloud Run service and use a load balancer.
Why it's wrong here
Cloud Run is good for stateless containers but lacks built-in ML serving features like batching.
✗
Use a single GKE cluster with multiple deployments and use Istio for routing.
Why it's wrong here
Managing GKE cluster adds complexity and may not be cost-effective for small-scale.
✓
Deploy all models to a single Vertex AI endpoint and configure traffic splitting.
Why this is correct
Vertex AI endpoints allow deploying multiple models behind one endpoint, sharing resources.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create separate endpoints for each model and use a load balancer to route traffic.
Why it's wrong here
Separate endpoints mean separate compute resources, increasing cost.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that separate endpoints or services are required for different models, when in fact Vertex AI endpoints support multi-model deployment with traffic splitting to share resources and minimize cost.
Detailed technical explanation
How to think about this question
Vertex AI endpoints use a shared serving infrastructure where multiple models can be deployed to the same endpoint, and traffic splitting is implemented via a weighted round-robin mechanism at the endpoint's load balancer. Under the hood, the endpoint's underlying compute nodes (e.g., TPU or GPU VMs) are shared across models, and the Vertex AI Prediction service handles model selection based on the traffic split percentages. In a real-world scenario, if one model has low latency requirements and another has high memory needs, traffic splitting allows you to allocate resources proportionally without over-provisioning.
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
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Deploy all models to a single Vertex AI endpoint and configure traffic splitting. — Vertex AI endpoints support traffic splitting, allowing you to deploy multiple models behind a single endpoint and route a percentage of traffic to each model. This enables resource sharing and cost optimization because the underlying compute infrastructure is shared among the models, unlike separate endpoints which would each require dedicated resources.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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