Databricks-Generative-AI-Engineer-Associate

The Databricks Generative AI Engineer Associate Exam (DGAE-A) covers foundational knowledge of Generative AI concepts and how they are implemented using Databricks Lakehouse platform. The exam focuses on the following key areas:

1. Introduction to Generative AI:

• Understanding the Basics: Define Generative AI, explain different model types (e.g., diffusion, transformers), and recognize key concepts like text generation, image generation, and code generation.
• Model Architectures and Training: Understand the different model architectures used for Generative AI (e.g., GPT, Stable Diffusion, DALL-E), their training processes, and common techniques like fine-tuning and transfer learning.
• Applications and Use Cases: Explore the applications of Generative AI in various industries, including content creation, marketing, design, healthcare, and more.

2. Databricks Lakehouse Platform for Generative AI:

• Databricks Fundamentals: Become familiar with the Databricks platform, including its core components (e.g., SQL, Delta Lake, MLflow), and their role in supporting Generative AI workloads.
• Data Management and Preparation: Understand how to efficiently manage and prepare data for Generative AI models, including data cleaning, transformation, and feature engineering.
• Model Building and Training: Learn how to build and train Generative AI models within the Databricks environment using libraries like Hugging Face Transformers and TensorFlow.
• Model Deployment and Serving: Gain knowledge on deploying and serving trained Generative AI models for real-time applications, utilizing Databricks features like Model Registry and MLflow.

3. Generative AI Best Practices and Responsible AI:

• Ethical Considerations: Understand the ethical implications of Generative AI, including bias mitigation, responsible AI principles, and data privacy.
• Model Evaluation and Monitoring: Learn how to evaluate generative AI models using appropriate metrics and monitor their performance for continuous improvement.
• Security and Governance: Understand the security considerations involved in working with Generative AI models and learn about data governance and regulatory compliance.

4. Applied Generative AI use cases:

• Text Generation: Understand common text generation tasks and how to apply Generative AI models for tasks like summarizing text, writing content, and creating chatbot responses.
• Image Generation: Learn about different image generation techniques and how to utilize Generative AI for generating images, editing existing images, and creating new visual content.
• Code Generation: Explore the use of Generative AI for automatically generating code, debugging existing code, and suggesting code improvements.

Exam Format:

• Multiple-choice questions
• Scenario-based questions
• Code snippets to analyze

The Databricks Generative AI Engineer Associate Exam is designed to assess your understanding of Generative AI concepts and your ability to apply them within the Databricks Lakehouse platform. Prepare well by studying the official Databricks documentation, online resources, and practice exams.