Tips for the Dataiku Generative AI Practitioner Certification

Dataiku offers an excellent set of free courses and certifications to help you learn the platform, from the basics to various specialties, while validating your proficiency in each track. The newest offering is the Generative AI Practitioner course and certification, which I recently completed.

In this post, I’ll share my experience to help you better understand what to expect and how to succeed.

Prerequisites for the Training Course

While the content is engaging and thoughtfully designed, I ran into a few setup surprises. Here are some ways to avoid potential hiccups with early preparation.

First, make sure you already have a solid foundation in Dataiku’s core features. If you’re new to the platform, I strongly recommend completing the Core Designer learning path first. The Generative AI Practitioner program builds on that foundation and assumes you’re already comfortable creating projects, datasets, recipes, and workflows.

Unlike most Dataiku courses, this one requires access to some tools outside the core platform. You’ll be configuring external connections, managing API keys, and building Python environments.

To complete all exercises successfully, you’ll need three main components:

• An LLM connection
• A SQL database connection
• A custom Python code environment

Let’s go through each.

1. LLM Connection

You’ll need API-level access to a large language model. Dataiku’s documentation walks you through the setup. The easiest option is to connect with OpenAI’s API.

I used an OpenAI API key, which required a billing account, but don’t worry, the data volumes and token usage throughout the course are minimal. The overall cost is negligible.

This connection powers several course exercises, including those related to the Dataiku Answers feature and other conversational AI interfaces.

2. SQL Database Connection

The Dataiku Answers module also requires a database to store user details and prompt history. You’ll need access to a database where you can create tables and write data. Any lightweight SQL instance (PostgreSQL, MySQL, SQLite, etc.) works fine, as long as you have the right permissions configured before starting. Without this, you’ll hit frustrating roadblocks midway through the conversational interface section.

3. Python Code Environment

The Retrieval-Augmented Generation (RAG) tutorial requires a Python environment that includes several specialized libraries. Ideally, these should run in the example project provided, but I had to create a custom Python environment to ensure compatibility. If you’re unfamiliar with that process, refer to my earlier tutorial for set-up guidance.

Here’s what you’ll need to install:

• langchain
• langchain_community
• chromadb
• pysqlite3-binary

I recommend building your environment up-front, confirming all packages are installed cleanly, and validating that your kernel starts successfully before beginning the RAG section. This small amount of setup work pays off later, unlocking the freedom to experiment with custom vector stores and retrieval pipelines as you advance.

What to Expect in the Certification Exam

The certification exam itself is concise, with only 20 questions. It’s more conceptually focused than hands-on, emphasizing understanding over implementation.

A few key things to know:

• You won’t need your SQL connection or custom Python environment during the exam.
• You will need your LLM connection active.
• The questions test your familiarity with Dataiku features, generative AI concepts, and the broader LLM workflow.

If you’re already comfortable with modern AI terminology (RAG, embeddings, prompt engineering, etc.) and have completed the exercises, reaching the 80% passing score should be straightforward.

Why It’s Worth It

Beyond the badge itself, this certification introduces how Dataiku operationalizes AI at scale. It doesn’t just teach you to call an API, it teaches you how to integrate and customize generative AI capabilities into enterprise workflows.

For data professionals, it’s a bridge between AI experimentation and AI enablement, between curiosity and implementation.

Before You Hit Start

Take the time to prepare your environment before you start. Once you do, the learning experience feels smooth, modern, and deeply relevant. You’ll come away not just with a certificate, but a working understanding of how to extend Dataiku into the world of generative AI. And that’s a skill set that’s becoming less optional by the day.