Databricks Generative AI Engineer Associate Exam Questions Help You Pass

Understand Databricks Generative AI Engineer Associate Exam Overview

The Databricks Generative AI Engineer Associate certification exam validates an individual’s ability to create and deploy generative AI solutions, specifically those using large language models (LLMs) and Databricks’ ecosystem. This certification focuses on building performant Retrieval-Augmented Generation (RAG) applications and LLM chains, leveraging Databricks tools like Vector Search, Model Serving, MLflow, and Unity Catalog.

Type: Online, proctored
Total Questions: 45 Multiple choice questions
Time Limit: 90 minutes
Cost: $200
Languages Available: English, Japanese (日本語), Brazilian Portuguese (Português BR), and Korean (한국어)

Master Databricks Generative AI Engineer Associate Key Topics

Databricks Generative AI Engineer Associate exam key topics cover the following details.

Design Applications (14%): Assess skills in breaking down complex AI tasks, selecting appropriate models and tools, and planning generative AI solutions.
Data Preparation (14%): Covers data ingestion, cleaning, and feature engineering for LLMs, along with setting up Vector Search for semantic searches.
Application Development (30%): Focuses on building and fine-tuning models, constructing LLM chains, integrating APIs, and developing RAG systems on Databricks.
Assembling and Deploying Applications (22%): Tests knowledge of deploying models using Databricks Model Serving, setting up APIs, and ensuring scalability and performance.
Governance (8%): Ensures understanding of data security and access management using Unity Catalog and compliance practices.
Evaluation and Monitoring (12%): Evaluates skills in monitoring application health, assessing model performance, and managing models with MLflow.

Databricks Generative AI Engineer Associate Exam Preparation Tips

Here are some effective tips to help you prepare for the Databricks Certified Generative AI Engineer Associate exam:

Familiarize Yourself with Databricks’ Generative AI Tools

Unity Catalog: Understand its role in data governance and how to set up access controls.
Vector Search: Practice using Vector Search for semantic similarity searches.
Model Serving: Get hands-on with deploying models and setting up API endpoints in Databricks.
MLflow: Review MLflow for model lifecycle management, including model tracking, versioning, and deployment.

Review Large Language Models (LLMs) and Generative AI Basics

Study how LLMs work and understand their applications in generative AI.
Learn to select and fine-tune LLMs for specific applications, focusing on Databricks’ capabilities to support these workflows.

Practice Data Preparation Techniques

Work on ingesting, cleaning, and preprocessing data for AI applications. Focus on transforming data into a usable format for LLMs.
Practice feature engineering and understanding how it impacts model performance.

Gain Hands-on Experience with RAG Applications

Practice building Retrieval-Augmented Generation (RAG) systems, integrating retrieval with generative models to create relevant outputs.
Familiarize yourself with designing and implementing LLM chains, using APIs and external databases to add value to your AI solutions.

Learn Key Governance and Security Practices

Study Unity Catalog for data security and access management.
Understand compliance standards for data handling, particularly in sensitive or regulated industries.

Develop Application Monitoring and Evaluation Skills

Set up monitoring tools for tracking model performance and application health, like response time and error rates.
Practice using MLflow for evaluating model accuracy, tracking performance metrics, and managing model updates.

Review the Exam Guide and Sample Questions

Review the official exam guide provided by Databricks, as it will cover exam objectives in detail.
Practice with Databricks Generative AI Engineer Associate sample questions if available to get familiar with the question style and time management.