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 company needs to serve a model for real-time predictions with a strict latency SLA of 100ms at the 99th percentile. The model is lightweight and traffic patterns are highly variable with occasional spikes. Which deployment strategy best meets the SLA while controlling cost?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy to Vertex AI Endpoint with min_replica_count=3 and autoscaling enabled.
Option D is correct because setting a minimum number of replicas ensures baseline capacity to handle initial spikes without cold start delays, while autoscaling handles larger spikes. Option A is wrong because batch prediction is not real-time. Option B is wrong because no scaling may cause over-provisioning or under-provisioning. Option C is wrong because Cloud Run with no accelerator may not meet latency SLA for ML models.
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.
✗
Deploy the model as a Cloud Run service with autoscaling to zero.
Why it's wrong here
Cloud Run may have cold start latency and no GPU option, potentially violating SLA.
✗
Deploy to Vertex AI Endpoint with manual scaling and a fixed number of replicas.
Why it's wrong here
Manual scaling cannot adapt to spikes and may waste resources.
✗
Use Vertex AI Batch Prediction.
Why it's wrong here
Batch prediction is asynchronous and not suitable for real-time.
✓
Deploy to Vertex AI Endpoint with min_replica_count=3 and autoscaling enabled.
Why this is correct
Min replicas provide baseline capacity to absorb spikes, and autoscaling adds replicas as needed.
Clue confirmation
The clue word "best" in the question point toward this answer.
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.
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 to Vertex AI Endpoint with min_replica_count=3 and autoscaling enabled. — Option D is correct because setting a minimum number of replicas ensures baseline capacity to handle initial spikes without cold start delays, while autoscaling handles larger spikes. Option A is wrong because batch prediction is not real-time. Option B is wrong because no scaling may cause over-provisioning or under-provisioning. Option C is wrong because Cloud Run with no accelerator may not meet latency SLA for ML models.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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