Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A team is selecting a foundation model for a text summarization use case. They need to consider factors that affect both model performance and production deployment. Which THREE factors are most critical? (Choose three.)
⚠ Common exam trap
Google Cloud often tests the distinction between model-centric factors (like parameter count) and deployment-centric factors (like latency and pricing), trapping candidates who assume bigger models are always better without considering operational constraints.
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
✓
Inference latency and throughput capabilities.
Inference latency and throughput are critical for production deployment because they directly determine the user experience and operational cost. A model with high latency may be unsuitable for real-time summarization, while low throughput limits the number of concurrent requests the system can handle, affecting scalability and cost-efficiency.
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 parameter count (billions of parameters).
Why it's wrong here
Model parameter count is a model-centric factor that influences capability, but it is not among the three most critical factors for production deployment. Operational factors like latency, context window, and pricing directly impact real-world feasibility.
- ✓
Inference latency and throughput capabilities.
Why this is correct
Inference latency and throughput are critical for production deployment because they directly determine user experience and operational cost. Low latency is essential for real-time summarization, and high throughput enables handling of concurrent requests efficiently.
- ✓
Context window length (maximum input tokens).
Why this is correct
Context window length determines the maximum amount of text the model can process at once. For summarization, a sufficiently long context window is often required to capture the full document, making it a critical factor for both performance and deployment.
- ✗
Training data provenance and licensing.
Why it's wrong here
Training data provenance and licensing are important for ethical and legal compliance, but they are not typically among the three most critical factors affecting model performance and production deployment in this context. Operational factors take priority.
- ✓
Pricing per token (input + output).
Why this is correct
Pricing per token affects the operational cost of running the model at scale. It is a critical deployment factor because cost can become prohibitive, especially for high-volume summarization tasks.
Go deeper
Related to this question
About these practice questions
This Generative AI Leader question is part of Courseiva's 683-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.