- A
Use Batch Prediction
Why wrong: Batch prediction is for offline processing, not real-time.
- B
Deploy to a Compute Engine VM
Why wrong: Compute Engine VMs are not optimized for ML inference latency out of the box.
- C
Deploy to Vertex AI Endpoint with a fixed number of replicas
Fixed replicas ensure always-on instances for low latency.
- D
Use MaaS (Model-as-a-Service) with autoscaling
Why wrong: Autoscaling can cause cold start latency.
Question 221 of 997
Optimize Real-Time Inference Latency with Fixed Replicas
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. 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 using Vertex AI Model Garden to deploy a Llama model. Which deployment option provides the best latency for real-time inference?
Quick Answer
The answer is deploying to a Vertex AI Endpoint with a fixed number of replicas, as this configuration provides the best latency for real-time inference on Vertex AI. By maintaining a constant pool of warmed-up replicas, you eliminate cold starts entirely, ensuring that every inference request is served immediately without the delay of spinning up new instances. On the Google Cloud Generative AI Leader exam, this question tests your understanding of deployment trade-offs: while autoscaling options like Model-as-a-Service (MaaS) can reduce costs, they introduce latency spikes during scale-up events, and batch prediction is designed for asynchronous workloads, not real-time responsiveness. A common trap is assuming autoscaling always optimizes performance, but for real-time inference, consistent low latency requires fixed replicas to pre-allocate resources. Memory tip: think “Fixed for Fast”—fixed replicas freeze out cold starts, giving you the fastest path to inference.
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
✓
Deploy to Vertex AI Endpoint with a fixed number of replicas
Option C is correct because deploying a Llama model to a Vertex AI Endpoint with a fixed number of replicas ensures that compute resources are pre-allocated and always warm, minimizing cold-start latency and providing consistent, low-latency responses for real-time inference. This approach uses a dedicated endpoint with persistent instances, which is optimized for sub-second response times required by interactive applications.
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.
- ✗
Use Batch Prediction
Why it's wrong here
Batch prediction is for offline processing, not real-time.
- ✗
Deploy to a Compute Engine VM
Why it's wrong here
Compute Engine VMs are not optimized for ML inference latency out of the box.
- ✓
Deploy to Vertex AI Endpoint with a fixed number of replicas
Why this is correct
Fixed replicas ensure always-on instances for low latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use MaaS (Model-as-a-Service) with autoscaling
Why it's wrong here
Autoscaling can cause cold start latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'autoscaling' (which optimizes cost) with 'low latency' (which requires pre-provisioned resources), and they overlook that MaaS with autoscaling introduces cold-start delays that are unacceptable for real-time inference.
Detailed technical explanation
How to think about this question
Vertex AI Endpoints use a gRPC-based prediction service with NVIDIA Triton Inference Server or TensorFlow Serving under the hood, enabling model-specific optimizations like dynamic batching and GPU tensor core utilization. Fixed replicas eliminate the cold-start penalty of loading the Llama model (which can be 7B–70B parameters) into GPU memory, which typically takes 10–30 seconds. In real-world scenarios, for a chatbot requiring p95 latency under 500ms, fixed replicas are essential to avoid the 2–5 second cold-start penalty that autoscaling would introduce.
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
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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: Deploy to Vertex AI Endpoint with a fixed number of replicas — Option C is correct because deploying a Llama model to a Vertex AI Endpoint with a fixed number of replicas ensures that compute resources are pre-allocated and always warm, minimizing cold-start latency and providing consistent, low-latency responses for real-time inference. This approach uses a dedicated endpoint with persistent instances, which is optimized for sub-second response times required by interactive applications.
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
This Generative AI Leader 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 Generative AI Leader exam.
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