- A
Use Vertex AI Model Garden to deploy the base PaLM 2 model.
Why wrong: Model Garden offers pre-built models, not custom tuned models.
- B
Wrap the model in a Cloud Function and invoke via HTTP.
Why wrong: Cloud Functions are not optimized for model inference and have cold start issues.
- C
Deploy the tuned model to a Vertex AI endpoint with GPU acceleration and autoscaling.
Dedicated endpoints with GPUs provide the lowest latency for real-time inference.
- D
Use Vertex AI Batch Prediction to process requests in batches.
Why wrong: Batch prediction is not real-time; it is for offline processing.
Question 733 of 997
Deploying Custom Models for Low Latency on Vertex AI
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.
A startup wants to deploy a custom-tuned large language model for real-time inference on Vertex AI. They need the lowest possible latency for end users. What deployment strategy should they choose?
Quick Answer
The answer is to deploy the tuned model to a Vertex AI endpoint with GPU acceleration and autoscaling. This strategy minimizes latency by dedicating GPU resources to process inference requests in real time, avoiding the cold starts and overhead of serverless options, while autoscaling ensures capacity matches demand without idle waste. On the Google Cloud Generative AI Leader exam, this question tests your understanding of deployment trade-offs for custom models: batch prediction is a common trap for asynchronous workloads, Cloud Functions introduces latency from initialization, and Model Garden’s PaLM 2 lacks support for custom tuning. The key insight is that low-latency, real-time inference requires a persistent, GPU-backed endpoint—not a stateless or batch service. Memory tip: think “GPU endpoint for real-time, batch for bedtime”—dedicated GPUs keep inference fast, while batch jobs can wait.
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 the tuned model to a Vertex AI endpoint with GPU acceleration and autoscaling.
Option C is correct because deploying a custom-tuned model to a Vertex AI endpoint with GPU acceleration and autoscaling provides the lowest possible latency for real-time inference. GPU acceleration enables parallel processing of inference requests, while autoscaling ensures sufficient compute resources are available to handle traffic spikes without cold starts. This combination minimizes both compute and network latency, which is critical for real-time user-facing 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 Vertex AI Model Garden to deploy the base PaLM 2 model.
Why it's wrong here
Model Garden offers pre-built models, not custom tuned models.
- ✗
Wrap the model in a Cloud Function and invoke via HTTP.
Why it's wrong here
Cloud Functions are not optimized for model inference and have cold start issues.
- ✓
Deploy the tuned model to a Vertex AI endpoint with GPU acceleration and autoscaling.
Why this is correct
Dedicated endpoints with GPUs provide the lowest latency for real-time inference.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Vertex AI Batch Prediction to process requests in batches.
Why it's wrong here
Batch prediction is not real-time; it is for offline processing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often confuse 'lowest possible latency' with 'high throughput' or 'cost efficiency,' leading them to choose batch prediction (D) or serverless options (B) without recognizing that GPU-accelerated endpoints are specifically designed for sub-second inference.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI endpoints with GPU acceleration leverage NVIDIA CUDA cores and Tensor Cores to parallelize matrix operations in transformer-based LLMs, reducing inference time from seconds to milliseconds for a single request. Autoscaling in Vertex AI uses a target CPU utilization metric (default 60%) to dynamically adjust the number of replicas, but for GPU-accelerated endpoints, it monitors GPU utilization instead, ensuring that the model can handle burst traffic without over-provisioning. A real-world scenario where this matters is a customer support chatbot that must respond within 200ms to maintain user engagement; using a GPU-backed endpoint with autoscaling can achieve this, while batch prediction would introduce minutes of delay.
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 the tuned model to a Vertex AI endpoint with GPU acceleration and autoscaling. — Option C is correct because deploying a custom-tuned model to a Vertex AI endpoint with GPU acceleration and autoscaling provides the lowest possible latency for real-time inference. GPU acceleration enables parallel processing of inference requests, while autoscaling ensures sufficient compute resources are available to handle traffic spikes without cold starts. This combination minimizes both compute and network latency, which is critical for real-time user-facing 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
3 more ways this is tested on Generative AI Leader
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which THREE considerations are critical when deploying a generative AI model using Vertex AI Endpoints for a latency-sensitive application? (Choose THREE.)
hard- ✓ A.Model size and architecture
- B.Number of model versions
- ✓ C.GPU type and number
- ✓ D.Autoscaling configuration
- E.Number of model instances
Why A: Model size and architecture directly impact inference latency because larger models with more parameters require more computation per request. For latency-sensitive applications, choosing a smaller or distilled model (e.g., Gemma 2B vs. 27B) or using quantization can reduce response times. Vertex AI Endpoints serve the model as-is, so the model's inherent computational cost is the primary driver of per-request latency.
Variation 2. A team deployed a custom generative AI model using KServe on Google Kubernetes Engine (GKE) with the above configuration. They notice that the model is taking longer than expected to respond. What is the most likely cause?
medium- A.The CPU resource limits are too low
- B.The model is crashing due to insufficient memory
- ✓ C.The model requires more than 1 GPU for acceptable performance
- D.The container image is too large and takes time to pull
Why C: The configuration specifies 1 GPU, but the model requires more than 1 GPU for acceptable performance. KServe on GKE allocates GPU resources based on the `limits` field; if the model's inference workload exceeds the memory bandwidth or compute capacity of a single GPU, latency increases due to queuing and serialization. This is the most likely cause of the slow response time, as GPU-bound models are sensitive to under-provisioning.
Variation 3. A data scientist is using Vertex AI Model-as-a-Service (MaaS) to deploy a fine-tuned open-source model. They notice high latency during inference. What is the most likely cause?
medium- A.The model is too large for the hardware
- ✓ B.The endpoint is set to autoscaling with a low minimum node count
- C.The model is not quantized
- D.The region is incorrect
Why B: High latency during inference on Vertex AI MaaS is most often caused by the endpoint scaling configuration. When autoscaling is enabled with a low minimum node count, the system may need to provision additional nodes to handle the request load, which introduces cold-start latency. This is especially pronounced for fine-tuned open-source models, which can be large and take time to load onto new nodes.
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Last reviewed: Jul 4, 2026
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