Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
Which TWO strategies can effectively reduce the operational costs of a generative AI model in production without significantly degrading user experience?
⚠ Common exam trap
Google Cloud often tests the misconception that increasing batch sizes or retraining frequency inherently reduces costs, when in fact these actions typically increase resource usage or introduce operational overhead without guaranteeing cost savings.
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
✓
Cache frequent prompt completions
Caching frequent prompt completions reduces operational costs by eliminating redundant inference calls for identical or similar user requests. This directly lowers compute usage and latency without degrading user experience, as cached responses are served instantly. It is a common optimization in production LLM deployments, especially for high-traffic applications with repetitive queries.
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 larger batch sizes for inference
Why it's wrong here
Batching is not always applicable for real-time responses and may increase latency.
- ✗
Increase the frequency of model retraining to improve efficiency
Why it's wrong here
Retraining costs money and may not reduce inference cost.
- ✓
Cache frequent prompt completions
Why this is correct
Caching reduces duplicate inference calls, lowering cost.
- ✓
Adopt a pay-per-use pricing model instead of a flat rate
Why this is correct
Pay-per-use ensures you only pay for actual usage.
- ✗
Deploy multiple models and route requests by complexity
Why it's wrong here
Managing multiple models increases operational overhead.
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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.