Exam objective
GAILGrounding techniques and their use cases
This objective sits in Techniques to improve gen AI model output, which carries 20% of the Generative AI Leader exam. The questions below are original, written from the official objective title above, and each explanation cites the Google Cloud page it rests on.
Objective title verbatim from the official objectives. Google Cloud exam page ↗
A worked example
Shown solved, with the whole explanation open: this is what every question here carries.
A leader wants model outputs connected to verifiable sources of information. Which technique is this?
Correct.
Concept
Grounding connects the AI's output to verifiable sources of information.
Why D
Connecting outputs to verifiable sources is grounding.
Source
Google Cloud, Generative AI Leader study guide, checked August 2026Grounding: Connecting the AI's output to verifiable sources of information.
Now you: practice questions
No account needed. The explanation opens when you answer.
Sample question 1 of 3
In a RAG pipeline, which step retrieves relevant information from external sources using tooling?
Sample question 2 of 3
In RAG, which step incorporates the retrieved information into the prompt sent to the model?
Sample question 3 of 3
A business worries about outdated answers because the model only knows its pretraining data. How does RAG help, per the vendor?
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Continue practisingRead the sources
These are the official pages the questions above cite. Reading them is studying the objective from the primary source, which is what the explanations point you toward anyway.