- A
Choose a machine type with GPUs for compute-intensive models.
GPUs reduce inference latency for deep learning models.
- B
Enable logging and monitoring for the endpoint.
Monitoring helps detect issues and optimize performance.
- C
Use a custom endpoint with a static IP address.
Why wrong: Static IP does not improve availability or latency.
- D
Deploy with min_replicas=2 and max_replicas=10 across multiple zones.
Multiple zones provide HA; autoscaling handles load.
- E
Deploy to a single zone to reduce network latency.
Why wrong: Single zone is a single point of failure; use multiple zones.
Question 366 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
You are deploying a model on Vertex AI and need to ensure high availability and low latency. Which THREE configurations should you implement?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Choose a machine type with GPUs for compute-intensive models.
Option A is correct because GPUs are essential for compute-intensive models, such as deep neural networks, as they provide parallel processing capabilities that significantly reduce inference latency compared to CPUs. On Vertex AI, selecting a machine type with GPUs (e.g., n1-standard-4 with NVIDIA T4) ensures that the model can handle high-throughput requests with low latency, which is critical for real-time serving.
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.
- ✓
Choose a machine type with GPUs for compute-intensive models.
Why this is correct
GPUs reduce inference latency for deep learning models.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enable logging and monitoring for the endpoint.
Why this is correct
Monitoring helps detect issues and optimize performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a custom endpoint with a static IP address.
Why it's wrong here
Static IP does not improve availability or latency.
- ✓
Deploy with min_replicas=2 and max_replicas=10 across multiple zones.
Why this is correct
Multiple zones provide HA; autoscaling handles load.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy to a single zone to reduce network latency.
Why it's wrong here
Single zone is a single point of failure; use multiple zones.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse static IP addresses with reliability, not realizing that Vertex AI endpoints already provide a stable DNS name with built-in load balancing, and that single-zone deployments are a common anti-pattern for high availability.
Detailed technical explanation
How to think about this question
Vertex AI uses a regional endpoint with an internal HTTP(S) load balancer that distributes traffic across replicas in multiple zones. Setting min_replicas=2 ensures at least two replicas are always running, providing redundancy, while max_replicas=10 allows automatic scaling based on CPU/GPU utilization or request latency. Under the hood, Vertex AI uses the Kubernetes-based AI Platform Prediction serving stack, where replicas are Pods managed by a HorizontalPodAutoscaler that adjusts count based on metrics like 'custom.googleapis.com|prediction|latency'.
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
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
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.
- →
Serving and Scaling Models — study guide chapter
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Serving and Scaling Models practice questions
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FAQ
Questions learners often ask
What does this PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Choose a machine type with GPUs for compute-intensive models. — Option A is correct because GPUs are essential for compute-intensive models, such as deep neural networks, as they provide parallel processing capabilities that significantly reduce inference latency compared to CPUs. On Vertex AI, selecting a machine type with GPUs (e.g., n1-standard-4 with NVIDIA T4) ensures that the model can handle high-throughput requests with low latency, which is critical for real-time serving.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 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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