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Generative AI Leader Practice Question: Build an internal knowledge base that allows…
A company wants to build an internal knowledge base that allows employees to ask questions about company policies in natural language. The knowledge base is stored in a Google Cloud SQL database. Which architecture should they use?
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 Agent Builder with Grounding to connect to Cloud SQL
Vertex AI Agent Builder with Grounding allows connecting to the database and answering questions based on its content, providing a natural language interface.
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 Gemini API with a prompt that includes all policies
Why it's wrong here
Cloud SQL policy tables far exceed any context window, so stuffing every policy into a prompt truncates content and cannot retrieve the relevant rows. The Gemini API is for generation, not database querying. It would suit a small, static policy set that fits entirely within the prompt.
- ✗
Use AutoML Natural Language to classify questions
Why it's wrong here
AutoML Natural Language trains classification models that assign predefined labels to text; it cannot retrieve policy rows from Cloud SQL nor generate natural-language answers. Classification suits routing or triaging incoming questions by topic, not answering them from stored policy content.
- ✗
Export Cloud SQL to BigQuery and use BigQuery ML
Why it's wrong here
BigQuery ML trains and runs models inside BigQuery over exported data; it offers no retrieval of Cloud SQL rows and no natural-language answer generation. Exporting also introduces staleness. BigQuery ML would be correct for forecasting or classification over large analytical datasets, not a live policy question-answering service.
- ✓
Use Vertex AI Agent Builder with Grounding to connect to Cloud SQL
Why this is correct
Vertex AI Agent Builder with Grounding queries Cloud SQL directly, retrieving policy rows as grounding context before generation. This satisfies the natural-language requirement while keeping answers anchored to the live database rather than a static index.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.