Vertex AI Search is the correct choice because it delivers enterprise-grade natural language search across private repositories, supports connectors to many data sources, and learns from user interactions such as clicks and ratings to continually improve result relevance.

This service is designed for enterprise search use cases where content is spread across documents, FAQs, and transcripts. It understands user intent with semantic retrieval, enforces access control over private data, and offers out-of-the-box ranking that can be tuned using implicit and explicit feedback so results get better over time. It also integrates with retrieval augmented generation patterns to power answer synthesis when needed.

Vertex AI Vector Search is a managed vector database for similarity search and indexing which is powerful infrastructure but it does not by itself provide an end-to-end enterprise search experience with connectors, relevance tuning from click and rating signals, or turnkey ranking. You would need to build significant application logic on top to match the requirements.

Google Search targets public web content and is not intended for secure retrieval over private enterprise repositories, nor does it provide enterprise-specific connectors or relevance tuning based on your organization’s feedback data.

Vertex AI Agent Builder is an umbrella product for building AI agents and experiences. The specific capability that satisfies enterprise search across private data with feedback-driven relevance is the dedicated search product, therefore the more precise choice for this scenario is the search service itself.

Full AWS Practitioner Certification Question