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Generative AI Leader Practice Question: Ground Gemini responses with real-time Google…

A company wants to ground Gemini responses with real-time Google Search results to improve accuracy of current events. Which feature enables this?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

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Google Search grounding

Google Search grounding connects Gemini to live search results to reduce hallucinations and provide up-to-date information.

Answer analysis

For each option: why learners choose it and why it is or isn't the right answer here.

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BigQuery ML
Why it's wrong here
BigQuery ML builds and runs machine-learning models inside BigQuery; it cannot retrieve live web results. Grounding with Google Search is the feature that injects current search data into Gemini prompts. BigQuery ML suits training or predicting on warehouse data, not grounding a model in real-time external events.
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Vertex AI Vector Search
Why it's wrong here
Vector Search performs similarity retrieval over embedded documents in an index; it cannot query live Google Search. It is tempting because it grounds responses in your own data, and it would be the correct choice for retrieval-augmented generation over a private corpus rather than current events.
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Gemini API function calling
Why it's wrong here
Function calling lets the model invoke developer-defined APIs, requiring you to build and wire a search integration yourself; it does not natively ground responses in Google Search. It is tempting because it extends model capabilities, and it would be correct for calling custom backend services or bespoke tools.
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Google Search grounding
Why this is correct
Google Search grounding connects Gemini to live Google Search results, injecting current web data into the model's context to improve factual accuracy on recent events. This satisfies the stem's requirement for real-time grounding, unlike static training data or retrieval from private corpora.

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