The correct answer is to change the machine type to n1-standard-8 and enable autoscaling with min replicas=1, max replicas=5. This solution directly addresses the high CPU utilization by providing more cores per instance, which reduces per-request processing time, while autoscaling dynamically adds replicas during peak hours to distribute the inference load and prevent timeouts. On the Google Cloud Generative AI Leader exam, this scenario tests your understanding of scaling Vertex AI deployments for latency—specifically, the trade-off between vertical scaling (larger machines) and horizontal scaling (more replicas) to optimize cost and performance. A common trap is choosing only to increase machine size without autoscaling, which wastes resources during off-peak hours, or enabling autoscaling without addressing the CPU bottleneck. Remember the memory tip: “Bigger box for the burst, more boxes for the load”—upgrade the machine to handle the compute demand, then scale out replicas to absorb traffic spikes.
Generative AI Leader Fundamentals of Generative AI Practice Question
This Generative AI Leader practice question tests your understanding of fundamentals of generative ai. 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.
You are an ML engineer at a retail company. You have deployed a generative AI model on Vertex AI to generate product descriptions. The model uses a custom container and is deployed to a single endpoint. Recently, you noticed that inference latency has increased significantly during peak hours, causing timeouts. You have checked the logs and found that the CPU utilization on the deployed instances is consistently above 90% during peak hours. The model is currently deployed with a single machine type (n1-standard-4) and no scaling. You need to reduce latency without incurring excessive cost. What should you do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change the machine type to n1-standard-8 and enable autoscaling with min replicas=1, max replicas=5
Option C is correct because upgrading to a larger machine (n1-standard-8) provides more CPU cores to handle the increased inference workload, while enabling autoscaling (min=1, max=5) allows the deployment to dynamically add replicas during peak hours to distribute the load and reduce latency. This combination addresses the high CPU utilization without over-provisioning during off-peak times, thus controlling cost.
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.
✗
Optimize the model using quantization and reduce the number of replicas
Why it's wrong here
Quantization helps but reducing replicas worsens latency.
✗
Switch to batch prediction instead of online prediction
Why it's wrong here
Batch prediction is not real-time.
✓
Change the machine type to n1-standard-8 and enable autoscaling with min replicas=1, max replicas=5
Why this is correct
More CPU and autoscaling handle peak load efficiently.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add a GPU accelerator to the existing machine
Why it's wrong here
GPU may be overkill and expensive.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume adding a GPU (Option D) is always the best way to reduce inference latency, but for CPU-bound models with high utilization, scaling out with more replicas and a larger CPU machine is more cost-effective and directly addresses the bottleneck.
Detailed technical explanation
How to think about this question
Vertex AI Prediction uses machine types like n1-standard-4 (4 vCPUs, 15 GB memory) and n1-standard-8 (8 vCPUs, 30 GB memory). Autoscaling in Vertex AI is based on target CPU utilization (default 60%) and can be configured with min/max replicas to handle traffic spikes. In this scenario, the model is likely a transformer-based text generator that is CPU-intensive; quantization (e.g., FP16 or INT8) could reduce memory bandwidth but the primary fix is horizontal scaling via autoscaling combined with vertical scaling to a larger machine type.
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.
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.
What does this Generative AI Leader question test?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change the machine type to n1-standard-8 and enable autoscaling with min replicas=1, max replicas=5 — Option C is correct because upgrading to a larger machine (n1-standard-8) provides more CPU cores to handle the increased inference workload, while enabling autoscaling (min=1, max=5) allows the deployment to dynamically add replicas during peak hours to distribute the load and reduce latency. This combination addresses the high CPU utilization without over-provisioning during off-peak times, thus controlling cost.
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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Question Discussion
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