Challenge 7: Conversational analytics for insights

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Target Persona: Data Analyst / Product Manager Estimated Duration: 30 minutes

Introduction

Disneyland park managers need to query this complex multi-silo dataset (reviews, wait times, graph movements, classifications) without writing SQL. In this challenge, you will build a data agent using BigQuery’s Conversational Analytics. We will link our agent to the Knowledge Catalog so it can access the technical and business metadata to help translate natural language questions into accurate queries.

Description

Task 7.1: Initialize the Conversational Analytics Agent

  1. In BigQuery Studio, navigate to the Agents tab.
  2. Create a new agent named disney_park_analyst and connect it to the tables within the disney dataset.
  3. In the Glossary section of the agent creation, add terms by importing them from the Knowledge Catalog.
  4. Add a Verified query, which will function as an example for the agent: join the attractions table with the wait-time forecasts.

Task 7.2: Execute Multi-Silo Prompts

Now that our agent is configured, it is time to test the agent in the chat interface. Ask complex, cross-dataset questions like:

  • « Which attractions have the highest negative sentiment today, and what is the most common path visitors take after leaving them? »

Success Criteria

To validate this challenge, you must demonstrate the following:

  • Show the Conversational Analytics agent disney_park_analyst configured in the BigQuery Console.
  • Show the verified queries and glossary terms you defined in the agent’s configuration.
  • Show a screenshot or proof of the chat interface successfully answering the complex multi-silo prompt without any SQL syntax errors.

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