Google Cloud Generative AI Leader

What the exam covers

Listed as the issuer lists them, with weights where the issuer publishes them.

• ›Fundamentals of gen AI (about 30%)
• ›Google Cloud's gen AI offerings (about 35%)
• ›Techniques to improve gen AI model output (about 20%)
• ›Business strategies for a successful gen AI solution (about 15%)

Key terms to know

Words from this certification's topic areas, in plain English. Know these and the study material will be much easier to follow.

Foundation model

A large model trained on broad data that can be adapted to many tasks.

Multimodal model

A model that handles more than text, such as images or audio.

Large language model (LLM)

A foundation model that reads and writes text.

Prompt engineering

Writing instructions so a model gives better answers.

Hallucination

When a model states something false in a confident way.

Retrieval-augmented generation (RAG)

Looking up relevant documents first, then giving them to the model to answer from.

Fine-tuning

Training an existing model further on your own examples.

Bias

Unfair skew in a model's results, often caused by skewed training data.

Responsible AI

Building and using AI fairly, safely, transparently and with accountability.

Return on investment (ROI)

The benefit you get compared with what it costs.

AI governance

The policies, roles and records that control how AI is built and used in an organisation.

Gemini

Google's family of AI models.

Build the knowledge here

Our lessons are not exam prep for this certification, but they cover the same ground.