Free Databricks Databricks-Certified-Generative-AI-Engineer-Associate Exam Questions

Become Databricks Certified with updated Databricks-Certified-Generative-AI-Engineer-Associate exam questions and correct answers

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Total 150 Questions | Updated On: Mar 26, 2025

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Question 1

Which benefits of Databricks Model Serving contribute to improving efficiency in managing machine learning models in production?

A : Reduce operational costs by leveraging serverless computing.

B : Streamline the ML lifecycle, enabling data science teams to focus on core tasks.

C : Support for a wide range of models, including custom, foundational, and external models.

D : Built-in A/B testing for model performance optimization.

E : All of these options.

Answer: E

Question 2

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?

A : Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to Unity Catalog using MLflow, and start a serving endpoint

B : Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint

C : Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container

D : Wrap the LLMs prediction function into a Flask application and serve using Gunicorn

Answer: B

Question 3

As a GenAI engineer, how would you describe the role of MLOps in managing machine learning systems?

A : MLOps is exclusively concerned with developing and testing machine learning models before they are deployed into production.

B : MLOps is limited to managing code repositories and version control for machine learning projects.

C : MLOps primarily focuses on automating data collection processes and managing databases for business intelligence.

D : MLOps is automation for managing data, code and models to improve performance, stability and long-term efficiency of ML systems.

Answer: D

Question 4

How can a Generative AI Engineer handle complex documents (with mixed text and images) for data extraction and chunking for a RAG based LLM application?

A : Using python libraries like PyPDF

B : Using Multi-modal models like OpenAI's GPT-4o and beyond

C : All of these options

D : Using layout model from Hugging Face

Answer: C

Question 5

As a Gen AI engineer, you are preparing to deploy a large application that integrates multiple LLMs and external GenAI services. What is a critical step to ensure the deployment is successful and the application runs smoothly in production?

A : Prioritize optimizing each model's performance before deployment to maximize efficiency.

B : Concentrate on reducing the overall application size to ensure faster deployment and lower resource usage.

C : Package the entire application together, including all models, endpoints, and necessary configurations, to ensure that all components function cohesively as part of a unified deployment pipeline.

D : Focus on deploying each LLM model separately to ensure flexibility and modular updates.

Answer: C

Page: 1 / 30

Total 150 Questions | Updated On: Mar 26, 2025

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