Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
Which of the following is a key consideration when selecting a GenAI model for a cost-sensitive application?
⚠ Common exam trap
Google Cloud often tests the misconception that model size (parameters) is the primary cost driver, but the exam emphasizes that operational metrics like latency and throughput are the direct determinants of infrastructure cost in production.
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
✓
Latency and throughput requirements
For cost-sensitive applications, latency and throughput requirements directly impact infrastructure costs, as lower latency often requires more expensive compute resources (e.g., higher GPU memory, faster inference hardware) and higher throughput may necessitate scaling out instances. Model size in parameters is a secondary factor that influences latency and throughput, but the primary cost driver is the operational performance needed to meet service-level agreements (SLAs).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model size in parameters
Why it's wrong here
While model size influences cost, it is not the only factor; latency and throughput are more direct for cost-sensitive apps.
- ✓
Latency and throughput requirements
Why this is correct
Latency and throughput directly determine the infrastructure needed and thus the cost per inference.
- ✗
Number of training epochs
Why it's wrong here
Training epochs are irrelevant for inference cost.
- ✗
The model's training data source
Why it's wrong here
Data source affects model performance, not operational cost.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.