Preparation Guide for Databricks Generative AI Engineer associate Certification

The Databricks Generative AI Associate Certification assesses your knowledge in building, deploying, and scaling AI models, particularly focusing on Retrieval-Augmented Generation (RAG) and other generative AI practices. Having recently cleared this certification, I’d like to share a preparation guide outlining key topics, study resources, and valuable tips to help you succeed.

Exam Structure and Real-World Applications

The exam comprises multiple-choice questions structured around real-world scenarios that require informed decision-making. You’ll be evaluated on your ability to:

• Choose the most suitable language model (LLM) for specific use cases.
• Determine the optimal architecture, such as RAG or agentic workflows.
• Make decisions regarding chunking, evaluation, and deployment strategies.

Each scenario will challenge you to apply your knowledge of Databricks’ AI capabilities and assess your understanding of best practices. For instance, you may be asked to identify the best retrieval strategy or select the appropriate prompting technique (zero-shot vs. few-shot). This real-world focus ensures you're well-prepared to implement AI solutions in production environments.

The official Databricks certification page provides a detailed exam guide, including recommended prerequisites, which I found helpful during my preparation. The guide outlines core topics and tools you’ll need to master, such as RAG, model serving, and vector search.

For more information about the exam and to access official resources, visit the Databricks Certification Page.

Key Topics to Study

1. Prompt Engineering

Prompt engineering is emphasized in the certification. You may encounter questions that focus on enhancing model responses through:

• Zero-shot Prompting: The model generates responses without any examples.
• Few-shot Prompting: Providing a few examples to improve accuracy.
• Prompt Chaining
• system and user prompt

Understanding how and when to apply these techniques is essential for effectively using LLMs in real-world scenarios.

2. Understanding Retrieval-Augmented Generation (RAG)

RAG is an approach that enhances language models by incorporating external knowledge retrieved from documents, databases, or structured information. For the certification, grasping how RAG functions and its implementation within Databricks is crucial.

How RAG Works:

• Parsing: Extracting the required information from sources which includes PDF or any other document..
• Chunking Strategies: Splitting large documents into smaller, retrievable chunks. Optimistic context size according to the model size.
• Retrieval: Using vector embeddings (via Databricks Vector Search) to fetch relevant external documents.
• Generation: The model generates answers based on the retrieved content.

End-to-End RAG in Databricks:

• Parsing & Chunking: Various python libraries used for parsing different types of document and identifying the chunking size and overlapping window size. Chunking strategies such a sentence split or paragraph split.
• Vector Search: Databricks Vector Search indexes document embeddings for efficient retrieval during RAG execution. Different types of embedding provided by vector search.
• Environment-Specific Authentication: Understand how to manage authentication through service principals and workspace tokens in both dev and prod.

Useful References:

• RAG Quality Data Pipeline
• Create and Query Vector Search
• RAG on Structured Data with Databricks Feature Store

3. RAG on Structured Data

While RAG is often associated with unstructured text, Databricks supports RAG on structured data, such as databases and tables. This knowledge is vital for scenario-based questions requiring augmentation of responses with structured information (e.g., customer data or inventory levels).

Useful Reference:

• RAG on Structured Data with Databricks Feature Store

4. Application Development

• Compound AI systems - Apart from model , connection to external sources
• Multistage reasoning- Multiple tasks that requires manual call to external sources .Framework for multistage reasoning. Define Action to take and task to be done in sequence

Agentic AI- Agent will automatically decide what action to take and which task to execute for specific use case.

• Agentic Design patterns
• Agent workflow
• Building agent using libraries such as langchain, llamaindex, openAI agents.

5. LangChain Framework

The LangChain framework simplifies the creation of chains in generative AI applications:

• Structured Approach: LangChain provides a structured methodology to connect different components and steps within a generative AI pipeline, enhancing workflow efficiency.
• Core Components: The main components of LangChain include prompts, models, retrievers, and tools, each customizable for specific needs.
• Example Workflow: A typical LangChain workflow involves retrieving relevant documents using a retriever, processing the text with a language model, and post-processing the output to generate the final response.

6. Model Deployment

Building and deploying scalable AI applications is a central focus of the certification. Key concepts include:.

• Model Deployment: Understand how to utilize Model Serving in Databricks, allowing you to create APIs for real-time predictions. Be prepared to monitor performance and manage production workloads effectively including deployment patterns (batch and streaming).Authentication to use for model serving in both dev and production
• Optimizing Performance: Familiarize yourself with resource management and optimization strategies for high-performing applications.

Useful Reference:

• Model Serving in Databricks

7. Evaluation and Monitoring of LLMs

Monitoring and evaluating the performance of language models is crucial for maintaining reliability and effectiveness. Databricks along with MLflow provides tools and methodologies for evaluating LLMs through:

• Lakehouse Monitoring: Implement monitoring frameworks to track model performance and resource usage, including setting up metrics to assess the model's accuracy and responsiveness in production.
• Evaluation Techniques: Understand the evaluation metrics being used for assessing context and response such as perplexity, toxicity, LLM as a judge, Context precision, answer relevancy, faithfulness , checking prompt safety in Databricks, Dataset license, defining guardrails

Useful Reference:

• Lakehouse Monitoring Documentation
• LLM evaluation

Practice Questions and Resources

Most exam questions are scenario-based and focus on applying RAG techniques. To excel, I recommend reviewing the official study guide and practicing sample questions.

Resources:

• Databricks Academy Training: Start with the Generative AI Fundamentals course, followed by the Generative AI Engineering with Databricks course, which covers essential topics including Application Development and Data Preparation.
• Exam Guide: Thoroughly review the Databricks Exam Guide, as there is typically one question for each key concept.
• Practice Exam: While practice exam were unavailable when I took the exam, resources like Practice exams on Udemy, offering questions that closely resemble the actual certification exam.
• Databricks Summit Video: Watch the session on preparing for the Databricks Generative AI Associate certification for insights and practice questions: Get Ready to Be Databricks Certified - Generative AI Associate.

I suggest diving into these additional resources only after finishing the official training. The training lays a solid foundation, and these additional materials will deepen your understanding, while also helping you assess your progress.

Good luck, and happy studying!