The answer is automatic scaling to zero, native GPU support, and built-in model monitoring. These three factors are the key differentiators when choosing between Vertex AI Endpoints and Cloud Run for model serving because Vertex AI Endpoints is purpose-built for ML workloads, offering native GPU acceleration and integrated model monitoring for drift detection, while Cloud Run, as a serverless compute platform, excels at scaling to zero when idle—a feature Vertex AI Endpoints does not easily achieve. On the Google Professional Machine Learning Engineer exam, this question tests your ability to match deployment infrastructure to operational requirements, often appearing as a scenario where you must prioritize cost efficiency versus performance monitoring. A common trap is assuming both services have identical scaling behaviors or cost structures, but the exam emphasizes that Cloud Run’s scale-to-zero is inherent, whereas Vertex AI Endpoints requires manual configuration for similar idle savings. Remember the mnemonic “GPU, Monitor, Zero” to recall the three decisive factors: GPU support, monitoring capabilities, and zero-scaling behavior.
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.
Which THREE factors should be considered when choosing between using Vertex AI Endpoints and Cloud Run for model serving? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Built-in model monitoring
Options A, B, and C are key differentiators. Vertex AI Endpoints supports GPUs natively, Cloud Run has limited GPU support. Cloud Run inherently scales to zero, Vertex AI endpoints don't always scale to zero easily. Vertex AI Endpoints has built-in model monitoring, Cloud Run does not. Options D and E are less differentiating: both services have similar cost structures and container requirements.
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.
✓
Built-in model monitoring
Why this is correct
Vertex AI Endpoints integrates with Model Monitoring; Cloud Run requires custom implementation.
Related concept
Static NAT maps one inside address to one outside address.
✗
Complexity of model containerization
Why it's wrong here
Both require containerization; the complexity is comparable.
✗
Cost per request
Why it's wrong here
Both services have similar pricing models (pay per request + compute time); not a strong differentiator.
✓
GPU support
Why this is correct
Vertex AI Endpoints offers native GPU support; Cloud Run has limited GPU availability (preview).
Related concept
Static NAT maps one inside address to one outside address.
✓
Automatic scaling to zero
Why this is correct
Cloud Run scales to zero by default; Vertex AI Endpoints requires minReplicaCount=0 and may still incur costs.
Related concept
Static NAT maps one inside address to one outside address.
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.
Trap categories for this question
Similar concept trap
Both services have similar pricing models (pay per request + compute time); not a strong differentiator.
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: Built-in model monitoring — Options A, B, and C are key differentiators. Vertex AI Endpoints supports GPUs natively, Cloud Run has limited GPU support. Cloud Run inherently scales to zero, Vertex AI endpoints don't always scale to zero easily. Vertex AI Endpoints has built-in model monitoring, Cloud Run does not. Options D and E are less differentiating: both services have similar cost structures and container requirements.
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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Question Discussion
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