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Databricks Exam Databricks-Generative-AI-Engineer-Associate Topic 2 Question 5 Discussion

Actual exam question for Databricks's Databricks-Generative-AI-Engineer-Associate exam
Question #: 5
Topic #: 2
[All Databricks-Generative-AI-Engineer-Associate Questions]

A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.

Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?

Show Suggested Answer Hide Answer
Suggested Answer: B

Problem Context: The goal is to deploy a trained LLM on Databricks in the simplest and most integrated manner.

Explanation of Options:

Option A: This method involves unnecessary steps like logging the model as a pickle object, which is not the most efficient path in a Databricks environment.

Option B: Logging the model with MLflow during training and then using MLflow's API to register and start serving the model is straightforward and leverages Databricks' built-in functionalities for seamless model deployment.

Option C: Building and running a Docker container is a complex and less integrated approach within the Databricks ecosystem.

Option D: Using Flask and Gunicorn is a more manual approach and less integrated compared to the native capabilities of Databricks and MLflow.

Option B provides the most straightforward and efficient process, utilizing Databricks' ecosystem to its full advantage for deploying models.


Contribute your Thoughts:

Jess
7 days ago
I'd go with option B. It's the most straightforward and efficient way to deploy the model on Databricks.
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Tenesha
10 days ago
I disagree, I believe the correct answer is D, as Flask and Gunicorn are commonly used for deploying models.
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Stephen
11 days ago
I think the answer is B, because MLflow is used for tracking and managing models.
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Annelle
19 days ago
Noah
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Elenora
6 days ago
That sounds like the correct option.
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Elenora
12 days ago
B
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