Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A company wants to use Generative AI for customer support chatbots. They are concerned about cost and latency. Which deployment option best balances these concerns?
⚠ Common exam trap
Google Cloud often tests the misconception that 'larger model = better accuracy always' or that 'on-premise is always cheaper,' ignoring the total cost of ownership, scaling overhead, and the efficiency gains from fine-tuning and caching for specific use cases.
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
✓
Use a fine-tuned version of a smaller model on Vertex AI with response caching
Using a fine-tuned smaller model on Vertex AI with response caching reduces both cost and latency. Smaller models require fewer computational resources, and caching avoids redundant inference calls, directly addressing the company's concerns without sacrificing accuracy for the specific task.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy an open-source model on-premise to avoid cloud costs
Why it's wrong here
On-premise deployment incurs significant infrastructure and maintenance costs, and latency may not be optimized without cloud TPUs.
- ✗
Rely on a third-party chatbot API that abstracts the model
Why it's wrong here
Third-party APIs can be convenient but often have per-query costs that scale linearly and may lack customization for specific business needs.
- ✗
Use the largest available foundation model via API for highest accuracy
Why it's wrong here
Larger models cost more per token and have higher latency, which may not be necessary for simple chatbot tasks.
- ✓
Use a fine-tuned version of a smaller model on Vertex AI with response caching
Why this is correct
A tuned smaller model reduces compute cost and caching minimizes repeated inference, lowering latency. Vertex AI provides scalable infrastructure.
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