How does the 'output length' setting affect a model's response?

It filters out inappropriate content

It determines the maximum number of tokens the model can generate

It controls the randomness of the generated text

It adjusts the model's vocabulary size

How does retrieval-augmented generation (RAG) improve the accuracy of model outputs?

By reducing the number of tokens generated

By increasing the model's context window size

By incorporating external knowledge into the generation process

By fine-tuning the model on a larger dataset

What is the purpose of Google Cloud RAG APIs?

To replace the need for vector databases

To provide a no-code interface for grounding

To automatically fine-tune models on retrieved data

To enable custom retrieval-augmented generation workflows

A government agency is building a public-facing assistant that answers questions about eligibility for social programs. The agency has a large internal database of program rules, but the rules change frequently. The assistant must provide answers that are always based on the current rules, and it must be able to explain which rule it used. Which approach best meets these requirements?

Use RAG to retrieve the current program rules from the internal database and ground each response in the retrieved text.

Ground with Google Search to find the rules online.

Use a high temperature setting to encourage diverse interpretations of the rules.

Fine-tune the model on the rules each time they change.

What is the primary purpose of setting a maximum token count in a model's response?

To improve the accuracy of the model's knowledge

To increase the creativity of the output

To limit the length of the generated response

Google CloudGenerative AI Leader

Domain 3Objective 3

3.3 Identify Grounding Techniques and Their Use Cases. GENERATIVE-AI-LEADER Practice Questions (Page 3)

Part of the Techniques to improve gen AI model output domain, which accounts for ~20% of the GENERATIVE-AI-LEADER exam.

34questions here

7free pages

10concepts

~20%of the exam

Questions 11–15

11foundation · easy
How does the 'output length' setting affect a model's response?
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12foundation · easy
How does retrieval-augmented generation (RAG) improve the accuracy of model outputs?
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13foundation · easy
What is the purpose of Google Cloud RAG APIs?
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14expert · hard
A government agency is building a public-facing assistant that answers questions about eligibility for social programs. The agency has a large internal database of program rules, but the rules change frequently. The assistant must provide answers that are always based on the current rules, and it must be able to explain which rule it used. Which approach best meets these requirements?
Select an answer first
15foundation · easy
What is the primary purpose of setting a maximum token count in a model's response?
Select an answer first

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