Databricks Databricks-Generative-AI-Engineer-Associate Practice Test Questions Answers
Exam Code: Databricks-Generative-AI-Engineer-Associate
(Updated 45 Q&As with Explanation)
Exam Name: Databricks Certified Generative AI Engineer Associate
Last Update: 15-Jan-2025
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Databricks-Generative-AI-Engineer-Associate Overview
Databricks-Generative-AI-Engineer-Associate Exam Overview
| Aspect | Details |
|---|---|
| Exam Content | The exam will focus on the following areas: |
| - Introduction to Generative AI: Concepts and types of generative models (e.g., GANs, VAEs) | |
| - Databricks Platform: Knowledge of how to use Databricks for AI and machine learning workflows. | |
| - Model Deployment: How to deploy AI models on Databricks. | |
| - Data Pipelines and Preprocessing: Implementing pipelines for data collection, cleaning, and processing for training AI models. | |
| - AI Workflow Orchestration: Using Databricks workflows for orchestrating the AI model lifecycle. | |
| Question Type | - Multiple-choice questions (testing conceptual understanding) |
| - Scenario-based questions (applying knowledge to practical scenarios) | |
| - Hands-on coding task (testing skills in deploying models or writing code related to AI workflows on Databricks) | |
| Exam Duration | 90 minutes |
| Total Number of Questions | Approximately 40-50 questions |
| Passing Score | 70% |
| Retake Policy | - If a candidate does not pass the exam, they must wait 7 days before retaking the exam. |
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Databricks Databricks-Generative-AI-Engineer-Associate Exam Topics Breakdown
| Topic | Details |
|---|---|
| Introduction to Generative AI | - Types of generative models (GANs, VAEs, etc.) |
| - Theories and algorithms behind generative AI | |
| Databricks for AI | - Basic Databricks concepts (clusters, notebooks, jobs, MLflow) |
| - Data handling on Databricks: Dataframes, Spark integration, and Databricks File System (DBFS) | |
| Data Pipelines | - Implementing ETL pipelines on Databricks |
| - Preparing data for model training: Data cleaning, transformation, and feature engineering | |
| Model Deployment | - Using Databricks for model training, evaluation, and deployment |
| - Integrating with tools like MLflow for managing the machine learning lifecycle | |
| AI Model Evaluation & Tuning | - Evaluating and fine-tuning generative AI models on Databricks |
| AI Workflow Orchestration | - Orchestrating workflows for machine learning and AI model deployment using Databricks Jobs and Databricks Workflows |
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