Databricks-Generative-AI-Engineer-Associate Exam Questions - Navigate Your Path to Success

The Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam is a good choice for Databricks Generative AI Engineers and if the candidate manages to pass Databricks Certified Generative AI Engineer Associate exam, he/she will earn Databricks Generative AI Engineer Associate Certification. Below are some essential facts for Databricks Databricks-Generative-AI-Engineer-Associate exam candidates:

In actual Databricks Certified Generative AI Engineer Associate (Databricks-Generative-AI-Engineer-Associate) exam, a candidate can expect 45 Questions and the officially allowed time is expected to be around 90 Minutes.
TrendyCerts offers 45 Questions that are based on actual Databricks-Generative-AI-Engineer-Associate syllabus.
Our Databricks-Generative-AI-Engineer-Associate Exam Practice Questions were last updated on: Mar 02, 2025

Sample Questions for Databricks-Generative-AI-Engineer-Associate Exam Preparation

Question 1

What is the most suitable library for building a multi-step LLM-based workflow?

APandas

BTensorFlow

CPySpark

DLangChain

Correct : D

Problem Context: The Generative AI Engineer needs a tool to build a multi-step LLM-based workflow. This type of workflow often involves chaining multiple steps together, such as query generation, retrieval of information, response generation, and post-processing, with LLMs integrated at several points.

Explanation of Options:

Option A: Pandas: Pandas is a powerful data manipulation library for structured data analysis, but it is not designed for managing or orchestrating multi-step workflows, especially those involving LLMs.

Option B: TensorFlow: TensorFlow is primarily used for training and deploying machine learning models, especially deep learning models. It is not designed for orchestrating multi-step tasks in LLM-based workflows.

Option C: PySpark: PySpark is a distributed computing framework used for large-scale data processing. While useful for handling big data, it is not specialized for chaining LLM-based operations.

Option D: LangChain: LangChain is a purpose-built framework designed specifically for orchestrating multi-step workflows with large language models (LLMs). It enables developers to easily chain different tasks, such as retrieving documents, summarizing information, and generating responses, all in a structured flow. This makes it the best tool for building complex LLM-based workflows.

Thus, LangChain is the most suitable library for creating multi-step LLM-based workflows.

Options Selected by Other Users:

Question 2

When developing an LLM application, it's crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.

Which action is NOT appropriate to avoid legal risks?

AReach out to the data curators directly before you have started using the trained model to let them know.

BUse any available data you personally created which is completely original and you can decide what license to use.

COnly use data explicitly labeled with an open license and ensure the license terms are followed.

DReach out to the data curators directly after you have started using the trained model to let them know.

Correct : D

Problem Context: When using data to train a model, it's essential to ensure compliance with licensing to avoid legal risks. Legal issues can arise from using data without permission, especially when it comes from third-party sources.

Explanation of Options:

Option A: Reaching out to data curators before using the data is an appropriate action. This allows you to ensure you have permission or understand the licensing terms before starting to use the data in your model.

Option B: Using original data that you personally created is always a safe option. Since you have full ownership over the data, there are no legal risks, as you control the licensing.

Option C: Using data that is explicitly labeled with an open license and adhering to the license terms is a correct and recommended approach. This ensures compliance with legal requirements.

Option D: Reaching out to the data curators after you have already started using the trained model is not appropriate. If you've already used the data without understanding its licensing terms, you may have already violated the terms of use, which could lead to legal complications. It's essential to clarify the licensing terms before using the data, not after.

Thus, Option D is not appropriate because it could expose you to legal risks by using the data without first obtaining the proper licensing permissions.