- A
Increase the maximum number of replicas in the autoscaling configuration to handle spikes
Why wrong: More replicas improve throughput but not per-request latency.
- B
Enable Vertex AI Model Caching and deploy the endpoint on a managed instance group with larger GPU nodes (e.g., A100 40GB)
Caching reduces computation for repeated prompts, and larger GPUs accelerate inference.
- C
Use preemptible VMs for the endpoint to get priority scheduling
Why wrong: Preemptibles can be terminated, causing latency spikes.
- D
Switch to a CPU-based n2-standard instance to reduce GPU contention
Why wrong: CPU serving would dramatically increase latency for such a large model.
Question 730 of 997
Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. 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.
A large enterprise is deploying a multi-modal generative AI application that processes customer support emails (text) and attached screenshots (images). They need to run inference on over 10,000 requests per minute with strict latency requirements (p99 < 500ms). They have already selected Gemini 1.5 Pro as the model and deployed it on Vertex AI using a GPU-based endpoint with autoscaling. During testing, they observe that the p99 latency spikes to over 2 seconds during peak traffic. The application is stateless and requests are independent. The team has access to Cloud Observability and can modify the deployment configuration. Which course of action should the team take to meet the latency requirements while minimizing cost?
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
✓
Enable Vertex AI Model Caching and deploy the endpoint on a managed instance group with larger GPU nodes (e.g., A100 40GB)
Vertex AI Model Caching reduces latency by caching the model's weights in GPU memory, eliminating the need to reload them for each request. Deploying on larger GPU nodes (A100 40GB) provides higher memory bandwidth and compute capacity, which directly addresses the p99 latency spike by ensuring the model can process more requests per second without queueing. This combination minimizes cost because it optimizes existing GPU utilization rather than simply adding more replicas, which would increase cost without fixing the root cause of latency.
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.
- ✗
Increase the maximum number of replicas in the autoscaling configuration to handle spikes
Why it's wrong here
More replicas improve throughput but not per-request latency.
- ✓
Enable Vertex AI Model Caching and deploy the endpoint on a managed instance group with larger GPU nodes (e.g., A100 40GB)
Why this is correct
Caching reduces computation for repeated prompts, and larger GPUs accelerate inference.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use preemptible VMs for the endpoint to get priority scheduling
Why it's wrong here
Preemptibles can be terminated, causing latency spikes.
- ✗
Switch to a CPU-based n2-standard instance to reduce GPU contention
Why it's wrong here
CPU serving would dramatically increase latency for such a large model.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume adding more replicas (Option A) is the universal fix for latency, but they overlook that the bottleneck is per-request inference time and cold-start delays, which require model caching and more powerful GPU nodes to reduce, not just horizontal scaling.
Detailed technical explanation
How to think about this question
Vertex AI Model Caching leverages NVIDIA's CUDA MPS (Multi-Process Service) or similar mechanisms to keep model weights resident in GPU VRAM, reducing the overhead of model loading from disk or network storage. In practice, for a model like Gemini 1.5 Pro, which has hundreds of billions of parameters, loading weights can take several seconds; caching eliminates this for subsequent requests. Real-world scenarios show that without caching, even with autoscaling, the first request to a new replica incurs a cold-start penalty that can spike p99 latency, especially under burst traffic.
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.
Visual reference
What to study next
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable Vertex AI Model Caching and deploy the endpoint on a managed instance group with larger GPU nodes (e.g., A100 40GB) — Vertex AI Model Caching reduces latency by caching the model's weights in GPU memory, eliminating the need to reload them for each request. Deploying on larger GPU nodes (A100 40GB) provides higher memory bandwidth and compute capacity, which directly addresses the p99 latency spike by ensuring the model can process more requests per second without queueing. This combination minimizes cost because it optimizes existing GPU utilization rather than simply adding more replicas, which would increase cost without fixing the root cause of latency.
What should I do if I get this Generative AI Leader 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.
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Last reviewed: Jul 4, 2026
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