Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A media company wants its internal knowledge assistant to answer employee questions using the company's own policy documents and past project reports, while keeping the Gemini model's general reasoning ability intact. The team has a large corpus stored in Cloud Storage and does not want to retrain or fine-tune the model. Which Google Cloud approach should they use?
⚠ Common exam trap
The trap here is assuming that feeding proprietary documents to Gemini requires fine-tuning, when retrieval-based grounding is the correct pattern for document question answering.
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
✓
Use Vertex AI Search with a data store pointing to the Cloud Storage corpus, and ground Gemini responses on the retrieved documents.
Grounding Gemini with Vertex AI Search connects the model to the company's own documents at query time, so responses reflect current policy and project material while the base model's general capabilities remain untouched. Because retrieval is separate from model weights, updating the corpus requires no retraining, which matches the team's stated constraint.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Vertex AI Search with a data store pointing to the Cloud Storage corpus, and ground Gemini responses on the retrieved documents.
Why this is correct
Vertex AI Search ingests and indexes the Cloud Storage corpus into a data store, then retrieves relevant passages at query time to ground Gemini. This supplies proprietary policy and project content without altering model weights, preserving the model's general reasoning. It is the intended Google Cloud pattern for enterprise retrieval-augmented generation over existing documents.
- ✗
Deploy Gemini on a larger machine type with more GPU memory so the model can ingest the full document corpus at runtime.
Why it's wrong here
Adding GPU memory increases the compute headroom for serving the model but does not give it access to the Cloud Storage corpus. Context windows are bounded regardless of machine size, and the entire corpus would not fit. This option mistakes infrastructure scaling for data grounding and leaves the assistant unable to cite internal policy content.
- ✗
Increase the Gemini model's temperature setting so it can explore the policy documents more broadly at inference time.
Why it's wrong here
Temperature controls randomness in token sampling; it has no connection to reading or retrieving external documents. Raising it would make answers less deterministic and more prone to fabrication, not more grounded in company policy. This option confuses a generation parameter with a retrieval capability and would degrade accuracy for an internal knowledge assistant.
- ✗
Fine-tune Gemini on the policy documents and past project reports using Vertex AI supervised tuning.
Why it's wrong here
Supervised fine-tuning changes model weights to learn a task style or format, not to store a large, changing document corpus. It would be costly to repeat whenever policies update, risks overfitting, and does not reliably return exact source passages. The team explicitly wants to avoid retraining, so fine-tuning is the wrong mechanism for document-grounded question answering.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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