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.
Which THREE considerations are critical when deploying a generative AI model using Vertex AI Endpoints for a latency-sensitive application? (Choose THREE.)
Model size and architecture
Larger models introduce higher latency.
Number of model versions
Why wrong: Model versioning does not directly affect latency.
GPU type and number
GPU selection impacts inference speed.
Autoscaling configuration
Proper autoscaling ensures low latency under varying load.
Number of model instances
Why wrong: While important, autoscaling handles instance count dynamically.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Model size and architecture
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Model size and architecture
Why this is correct
Larger models introduce higher latency.
Related concept
Read the scenario before looking for a memorised answer.
Number of model versions
Why it's wrong here
Model versioning does not directly affect latency.
GPU type and number
Why this is correct
GPU selection impacts inference speed.
Related concept
Read the scenario before looking for a memorised answer.
Autoscaling configuration
Why this is correct
Proper autoscaling ensures low latency under varying load.
Related concept
Read the scenario before looking for a memorised answer.
Number of model instances
Why it's wrong here
While important, autoscaling handles instance count dynamically.
Common exam traps
Google Cloud often tests the distinction between configuration choices that affect latency (GPU type, autoscaling, model size) versus operational or lifecycle management choices (version count, manual instance count) that do not directly impact per-request response time.
Detailed technical explanation
Under the hood, GPU type (e.g., NVIDIA L4 vs. A100) determines memory bandwidth and compute units, directly affecting time-to-first-token and tokens-per-second. Autoscaling configuration, such as min/max replicas and target CPU utilization, ensures that the endpoint can handle burst traffic without cold starts, which is critical for maintaining sub-100ms latency. In practice, a model like PaLM 2 on an A100 with aggressive autoscaling can serve real-time chat, while the same model on a T4 would fail latency SLAs.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
The correct answer is: Model size and architecture — 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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