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Databricks-Generative-AI-Engineer-Associate Exam Dumps : Databricks Certified Generative AI Engineer Associate

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Databricks Databricks-Generative-AI-Engineer-Associate Exam Dumps FAQs

Q. # 1: What is the Databricks Databricks-Generative-AI-Engineer-Associate Exam?

The Databricks Databricks-Generative-AI-Engineer-Associate Exam assesses an individual's ability to design and implement LLM-enabled solutions using Databricks tools.

Q. # 2: Who should take the Databricks-Generative-AI-Engineer-Associate Exam?

The Databricks-Generative-AI-Engineer-Associate exam is ideal for professionals seeking to validate their skills in designing and implementing Generative AI solutions using Databricks. It caters to individuals with experience in data engineering, machine learning, and a keen interest in Generative AI.

Q. # 3: What topics are covered in the Databricks-Generative-AI-Engineer-Associate Exam?

The Databricks-Generative-AI-Engineer-Associate exam delves into core areas of building Generative AI workflows on Databricks. Key topics include:

  • Data preparation for Generative AI models
  • Prompt engineering techniques
  • Retrieval-Augmented Generation (RAG)
  • Large Language Model (LLM) selection, chaining, and monitoring
  • Building Generative AI agents
  • Governance and security considerations for Generative AI

Q. # 4: How many questions are on the Databricks Databricks-Generative-AI-Engineer-Associate Exam?

The Databricks-Generative-AI-Engineer-Associate exam consists of 45 multiple-choice questions.

Q. # 5: How long is the Databricks-Generative-AI-Engineer-Associate Exam?

The Databricks-Generative-AI-Engineer-Associate exam has a time limit of 90 minutes.

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CertsTopics provides high-quality study materials, including Databricks-Generative-AI-Engineer-Associate exam dumps, questions and answers, PDFs, and practice tests specifically tailored for the Generative AI Engineer Exam. Our Databricks-Generative-AI-Engineer-Associate preparation materials are designed to help you grasp complex topics and test your knowledge effectively before exam day.

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Yes, CertsTopics ensures that all Databricks-Generative-AI-Engineer-Associate questions and answers are updated according to the latest exam syllabus and real-world industry trends.

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Databricks Certified Generative AI Engineer Associate Questions and Answers

Question 1

A Generative Al Engineer wants their (inetuned LLMs in their prod Databncks workspace available for testing in their dev workspace as well. All of their workspaces are Unity Catalog enabled and they are currently logging their models into the Model Registry in MLflow.

What is the most cost-effective and secure option for the Generative Al Engineer to accomplish their gAi?

Options:

A.

Use an external model registry which can be accessed from all workspaces

B.

Setup a script to export the model from prod and import it to dev.

C.

Setup a duplicate training pipeline in dev, so that an identical model is available in dev.

D.

Use MLflow to log the model directly into Unity Catalog, and enable READ access in the dev workspace to the model.

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Question 2

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?

Options:

A.

Switch to using External Models instead

B.

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

C.

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

D.

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

Question 3

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?

Options:

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 LLM’s prediction function into a Flask application and serve using Gunicorn