The answer is to deploy both versions to the same endpoint and set traffic_split to 90% for v1 and 10% for v2. This is the most efficient approach because Vertex AI Endpoints natively support traffic splitting between model versions, allowing you to gradually route a precise percentage of live inference requests to v2 without managing separate endpoints or external load balancers. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of Vertex AI’s built-in deployment features for A/B testing traffic splitting, a common pattern for safe model rollouts. A frequent trap is overcomplicating the solution by creating separate endpoints or using Cloud Load Balancing, which operates at the network layer and cannot split traffic at the model version level. Remember the memory tip: “One endpoint, split the percent” — keep your deployment simple and let Vertex AI handle the routing.
PMLE Serving and scaling models Practice Question
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 machine learning team wants to perform A/B testing between two model versions (v1 and v2) on Vertex AI Endpoint. They need to gradually route 10% of traffic to v2 while monitoring performance. What is the most efficient way to achieve this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy both versions to the same endpoint and set traffic_split to 90% for v1 and 10% for v2.
Option B is correct because Vertex AI Endpoint natively supports traffic splitting between model versions. Option A is wrong because creating separate endpoints adds complexity and cost. Option C is wrong because Cloud Load Balancing operates at the network level, not model level. Option D is wrong because batch prediction is not for real-time A/B testing.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use a Cloud Load Balancer to route traffic based on a header.
Why it's wrong here
Load balancer cannot split at the model version level within an endpoint.
✓
Deploy both versions to the same endpoint and set traffic_split to 90% for v1 and 10% for v2.
Why this is correct
Vertex AI Endpoint supports traffic splitting for A/B testing.
Related concept
Static NAT maps one inside address to one outside address.
✗
Create two separate endpoints and use a weighted DNS round-robin.
Why it's wrong here
Overly complex and not as precise as native traffic splitting.
✗
Run batch predictions for v2 and log results separately.
Why it's wrong here
Batch predictions are not real-time and do not serve live traffic.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related PMLE NAT questions on configuration and troubleshooting.
Serving and scaling models — This question tests Serving and scaling models — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Deploy both versions to the same endpoint and set traffic_split to 90% for v1 and 10% for v2. — Option B is correct because Vertex AI Endpoint natively supports traffic splitting between model versions. Option A is wrong because creating separate endpoints adds complexity and cost. Option C is wrong because Cloud Load Balancing operates at the network level, not model level. Option D is wrong because batch prediction is not for real-time A/B testing.
What should I do if I get this PMLE question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related PMLE NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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