Databricks Exam Databricks-Generative-AI-Engineer-Associate Topic 5 Question 4 Discussion

Actual exam question for Databricks's Databricks-Generative-AI-Engineer-Associate exam

Question #: 4
Topic #: 5

[All Databricks-Generative-AI-Engineer-Associate Questions]

A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application.

What strategy should the Generative AI Engineer use?

ASwitch to using External Models instead

BDeploy the model using pay-per-token throughput as it comes with cost guarantees

CChange to a model with a fewer number of parameters in order to reduce hardware constraint issues

DThrottle the incoming batch of requests manually to avoid rate limiting issues

Show Suggested Answer

Suggested Answer: B

Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.

Explanation of Options:

Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.

Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.

Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.

Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.

Option B is ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.

by Izetta at Oct 23, 2024, 02:14 AM

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Oliva

3 days ago

Hmm, I'm not so sure about that. Reducing the number of parameters might be a better idea to avoid hardware constraints. Option C looks promising.

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Rolande

4 days ago

I think option B is the way to go. Pay-per-token throughput sounds like a good cost-effective solution for this scenario.

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Tasia

19 days ago

I disagree, I believe deploying the model using pay-per-token throughput would be more cost-effective in the long run.

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Yolando

27 days ago

I think the best strategy would be to switch to using External Models instead.

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