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Google Cloud Generative AI Leader Generative AI Leader Practice Test

500 questions with instant explanations, domain breakdown, and wrong-answer analysis. Built for the real exam.

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Real exam: 90 min
Pass mark: 700%

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Q1Fundamentals of Generative AIeasy
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A startup is building a customer support chatbot using Vertex AI and wants to ground responses in their product documentation to reduce hallucinations. Which approach should they use?

Enable Vertex AI Grounding with a custom enterprise data store containing the documentation.Correct
BUse the Codey API for text generation.
CUse the base model without any grounding to maximize flexibility.
DFine-tune the model on the documentation and deploy.

Vertex AI Grounding with a custom enterprise data store is the correct approach because it allows the chatbot to retrieve and cite specific chunks from the product documentation in real time, directly reducing hallucinations by constraining responses to verified content. This met…Read full explanation

Q2Fundamentals of Generative AImedium
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A data scientist notices that a text generation model deployed on Vertex AI returns repetitive outputs after a few turns in a chat application. What is the most likely cause and the best parameter adjustment?

AThe max_output_tokens is too low; increase it to allow more diverse output.
The top_p value is too high; reduce top_p to limit token sampling.Correct
CThe model is overfitted; switch to a smaller model.
DThe temperature is too low; increase temperature to add randomness.

Repetitive outputs in a chat application after a few turns are typically caused by the model getting stuck in a loop due to high cumulative probability from top-p sampling. Reducing top_p limits the set of tokens considered at each step, forcing the model to explore less likely t…Read full explanation

Q3Fundamentals of Generative AIhard
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A financial services company wants to use generative AI to generate personalized investment advice. They must ensure responses comply with regulatory requirements (e.g., no guarantees of returns). Which Vertex AI safety feature should they primarily use?

AVertex AI Grounding with their compliance database.
BPrompt engineering with instructions to avoid guarantees.
Safety filters with a custom blocklist that includes phrases like 'guaranteed return'.Correct
DReinforcement learning from human feedback (RLHF) on the model.

Option C is correct because safety filters with a custom blocklist allow the company to define specific prohibited phrases (e.g., 'guaranteed return') that the model must avoid generating. This provides a deterministic, rule-based enforcement layer that directly addresses regulat…Read full explanation

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