Note (2026-05-06): Vertex AI was rebranded as Gemini Enterprise Agent Platform. Google's exam guides still use the Vertex AI naming, so this article does too. The official guides may switch to the new name at some point as you prep, but for now we're matching the language currently in the exam materials.
BigQuery ML is one of the easier topics on the Generative AI Leader exam, but it shows up in scenario questions where the right answer hinges on noticing that the data is already in BigQuery and the team writes SQL. Once you see that, the choice almost makes itself. I want to walk through what the service actually does, why it is described as low-code rather than no-code, and the specific scenario the exam expects you to flag.
BigQuery ML lets you build and run machine learning models directly inside BigQuery. The model lives where the data lives. You do not export rows to Cloud Storage, spin up a training environment, learn a framework like TensorFlow or PyTorch, and then wire the predictions back into your pipeline. All of that goes away. You write a query, training happens against the table in place, and the resulting model is queryable the same way a table is.
That collapse of the workflow is the whole pitch. A normal path from raw data to a trained model has a lot of moving parts. With BigQuery ML, the data warehouse is also the training environment and the prediction surface.
This is the distinction I want to make sure you internalize before exam day. BigQuery ML is SQL-based. Most data analysts already know SQL, so the on-ramp is dramatically shorter than a full machine learning engineering workflow. But the on-ramp is not zero. You are still writing queries. A statement like the one below is what training looks like:
CREATE OR REPLACE MODEL `project.dataset.my_model`
OPTIONS(model_type='linear_reg') AS
SELECT label, feature_1, feature_2
FROM `project.dataset.training_data`;That is code. Less code than a TensorFlow training loop, but code. The right way to describe BigQuery ML on the Generative AI Leader exam is low-code, not no-code. If an answer choice calls it no-code, that choice is wrong.
BigQuery ML is not heavily tested on the Generative AI Leader exam. You just need to know what it is and when you would reach for it. The scenario that fits is a data team whose data already sits in BigQuery, who wants to run machine learning on that data, and who would rather not stand up a separate Vertex AI training pipeline to do it. Those three signals together point at BigQuery ML. Pull any one of them out and the answer might shift to Vertex AI or to a managed model API instead.
That is the whole picture for this topic. My Generative AI Leader course covers BigQuery ML alongside the rest of the foundational material so the scenario clues click into place when you see them on the exam.
