Getting Databricks Generative AI Engineer Associate (and What I Learned)

[devipriya]

I just earned my Databricks Certified Generative AI Engineer Associate Certification, and in this post, I’m sharing the key tips, resources, and personal insights that helped me succeed.

My certiticate from Databricks

Navigation:

• Prelude
• About the cert
• Resources — the how
• Exam D day tips
• Personal Insights
• Outro

Prelude

As part of the solution and architecture team in my startup, it’s important for me to keep my GenAI stack up to date. While working on a recent RFP, I started exploring Databricks more deeply and that’s when my interest really grew. A platform that originally focused on data engineering has now evolved into a full-stack platform for building end-to-end AI solutions. That inspired me to spend more time learning and understanding its GenAI capabilities.

About the cert

This certificate is pretty new, introduced by Databricks in 2024. It is aimed at testing your knowledge across the following areas:

1. Design Applications — 14%
2. Data Preparation — 14%
3. Application Development — 30%
4. Assembling and Deploying Apps — 22%
5. Governance — 8%
6. Evaluation and Monitoring — 12%

For a detailed overview, access the complete 📚exam guide.

Resources — The How

Based on my experience, here are a few things to be ready with for this exam:

1️⃣ Hands-on experience with RAG and Agents
Preferably using open-source tooling such as LangChain and LangGraph. The exam is very practical, so building even a small demo project makes a big difference.

2️⃣ A fundamental understanding of how Databricks works
Start with the two free badges available on the Databricks Academy:

• Databricks Fundamentals Badge
• Generative AI Fundamentals Badge
  (Both are completely free — thanks, Databricks!)

3️⃣ Self-paced GenAI learning path (Databricks Customer Academy)
I completed the following four courses:

• Generative AI Solution Development (RAG)
• Generative AI Application Development (Agents)
• Generative AI Application Evaluation and Governance
• Generative AI Application Deployment and Monitoring

4️⃣ Demo notebooks and Labs
I also went through the hands-on demos and labs for each module.

Note: The self-paced courses are free to access, but the demo/lab notebooks require an annual subscription.

Exam D day tips

Here are some quick tips for you:

✅ I took the online exam — the process was smooth and well-organized.
✅ You’ll face ~56 multiple-choice questions, including unscored ones.
✅ Duration: 90 minutes

💡 Pro Tip: Use the “Mark for review later” feature. I used the full time to answer, review, and revise — and found it just right.

Personal Insights

Here are a few quick notes to give you a sense of the kinds of topics you’ll dive into as part of this exam:

1. Generative AI Solution Development (RAG)

Prompt Engineering Primer

A good prompt generally contains 4 parts:

1. Instruction — A clear directive
2. Context — Background
3. Input — Your specific question
4. Output — Your desired structure

• Zero vs Few-shot Prompting

Zero-shot Prompting — When not using any examples, vs Few-shot Prompting — When you provide a few input-output examples

• Prompt Chaining — This allows for complex tasks to be broken into manageable steps
• Tradeoffs with prompting — Despite being simple and efficient, the output is limited by the pre-trained model’s internal knowledge. For external knowledge, RAG is needed.

Introduction to RAG

RAG helps overcome prompting limitations by passing contextual information, much like taking an exam with open notes. Chances are you’ll answer better now that you have access to a known, reliable source of data.

The first important part for RAG implementation is data prep, as garbage in = garbage out.

RAG pipeline include:

1. Ingestion — Pre-processing — Data storage & Governance
2. Chunking — This is use case specific. Different variants exist, including context-aware and fixed chunking. You can use either or a combination. Experiment with different chunk sizes and (basic to advanced) approaches to find your right fit. For instance, windowed summarization is a context-enriching method, where each chunk includes a ‘windowed summary’ of the previous few chunks.
3. Embedding — best practice here is to choose the right embedding model based on your domain, and to use the same embedding model on the question and the retrieval side.
4. Storing in Vector Database — a database that is optimized to store and retrieve high-dimensional vectors such as embeddings. In the Databricks world, there is a 3-step process to set up vector search:

Step 1 — Create a Vector Search Endpoint

Step 2 — Create a Model Serving Endpoint (optional if you want to have Databricks compute the embeddings)

Step 3 — Create a Vector Search Index

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pic credits — Databricks

Evaluating a RAG application

In evaluating a rag application, you will have to check and evaluate the individual components (such as chunking performance, retrieval performance, and generator performance) along with the overall end-to-end solution.

RAG evaluation metrics include context precision, content relevancy, context recall, faithfulness, answer relevance, and answer correctness, and are based on the below 4 entities — Ground Truth, Query, Context, and Response.

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pic credits — Databricks

2. Generative AI Application Development (Agents)

• Real-world prompts have multiple intents, with each intent having multiple tasks.
• You first identify the intent. And then you implement the intent using chains.
• Frameworks like LangChain help create Gen AI applications that utilize large language models

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pic credits — Databricks

Agents

• An application to execute complex tasks by using language models to define a sequence of actions to take
• 4 design (agentic reassoning) patterns include react, tool use, planning (single, sequential, graph task), and multi-agent collaboration

Building Agentic Systems

To translate a business use case into an AI pipeline: Identify business goals → determine required data inputs → define expected outputs → map these to model tasks and chain components.

Pay-per-token vs Provisioned throughput

• Go with pay-per-token for low throughput and provisioned throughput for high throughput. At low usage, you only need occasional access, so pay-per-token keeps costs low by charging only for what you use.
• At high usage, the cost of pay-per-token becomes more expensive than reserving dedicated capacity, so provisioned throughput gives you a discounted, predictable rate for heavy/consistent workloads.

3. Generative AI Application Evaluation and Governance

To evaluate these complex AI systems, you will need to evaluate their components. The Data and AI Security Framework was developed to demystify AI security and is based on 12 AI components and 55 associated risks.

Two options to evaluate:

• If you have ground data set, go with Benchmarking, where you will compare models against standard evaluation data sets
• If you don’t have ground truth, define your custom metric and go with LLM-as-a-judge. Some best practices for LLM-as-a-judge :

— Use small rubric scales

— Provide a wide variety of examples

— Use a high-token LLM — more tokens equals more context

4. Generative AI Application Deployment and Monitoring

Offline vs Online Evaluation

Offline evaluation is everything that happens before launching the system in prod, whereas online evaluation is everything that happens after launching the system in prod

Evaluation vs Monitoring

In the Gen AI system lifecycle, post building your AI system, you evaluate it -> deploy it -> after which you start monitoring it.

• Evaluation: Before deployment, test models on benchmarks and datasets.
• Monitoring: After deployment, track real-world usage, drift, and performance metrics.

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pic credits — Databricks

Deployment Methods

Different use cases call for different types of deployment methods, such as batch, streaming, real-time, and edge/embedded. Each of these methods comes with its tradeoffs.

Recommended LLMOps Architecture

Similar to traditional software development, it is recommended to have 3 separate environments as depicted in the picture below.

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pic credits — Databricks

Outro

I enjoyed preparing for this exam — the GenAI Engineering pathway on Databricks Academy is extremely well curated. What made it even more exciting is how closely it maps to real-world workflows — from designing your AI system, to developing it, evaluating it, deploying it into production, and finally monitoring it. I’m confident I’ll be putting these skills to use immediately in my solutions and architectures. Definitely one of those certifications that translates straight into real-world impact!

[Khaja_Zaffer]

Thank you so much @devipriya 

[BR_DatabricksAI]

Thank you @devipriya for providing the detailed information on certifications.