Using a Serverless SQL endpoint can reduce the start-up time for the endpoint while managing costs. Serverless SQL endpoints automatically scale resources based on the workload, ensuring efficient utilization without needing manual intervention. This approach eliminates the need to manage cluster size or scaling values directly and helps minimize start-up delays by leveraging pre-existing resources.

The dashboard cannot be refreshed that quickly. Given the requirement to see updated results within one minute or less, the data analyst should caution about the potential limitations on the refresh rate of the dashboard itself. Many dashboard tools have restrictions or lag times in how quickly they can refresh data, which might not align with the rapid updates from the gold-level tables triggered every minute.

To directly ingest data from cloud-based object storage, you can create an external table while specifying the object storage path to LOCATION. The LOCATION parameter allows you to point to an external storage location, such as AWS S3, where the data resides. This approach integrates external data with your database system, making it accessible for query and analysis without copying the data into the database's internal storage.

To designate sections on a dashboard using text, the most straightforward and practical method is to use direct text written into the dashboard in editing mode. This allows the data analyst to clearly label each section (Development, Testing, and Production) without requiring additional tools or configurations. Separate endpoints, queries, or color palettes do not inherently fulfill the need for text-based section designation. While Markdown-based text boxes can be useful, it is more common to use direct text input in the dashboard environment.

Databricks SQL is designed for creating SQL queries and data visualizations and supports serverless compute resources. It also provides features for building dashboards, making it the most suitable choice for the requirements stated.