Salesforce Data-Architect Exam With Confidence Using Practice Dumps

According to the Salesforce documentation, a hub-and-spoke model is an integration architecture pattern that allows connecting multiple Salesforce orgs using a central org (hub) and one or more secondary orgs (spokes). The hub org acts as the master data source and orchestrates the data flow between the spoke orgs. The spoke orgs act as the consumers or producers of the data and communicate with the hub org.

To orchestrate data between the hub org and spoke orgs, a data architect should use:

A middleware solution that extracts and distributes data across both the hub and spokes (option A). This means using an external service or tool that can connect to multiple Salesforce orgs using APIs or connectors, and perform data extraction, transformation, and distribution operations between the hub and spoke orgs. This can provide a scalable, flexible, and reliable way to orchestrate data across multiple orgs.

Developing custom APIs to poll the hub org for change data and push into the spoke orgs (option B) is not a good solution, as it can be complex, costly, and difficult to maintain. It may also not be able to handle large volumes of data or complex transformations efficiently. Developing custom APIs to poll the spoke orgs for change data and push into the hub org (option C) is also not a good solution, as it can have the same drawbacks as option B. It may also not be able to handle conflicts or errors effectively. Using a backup and archive solution that extracts and restores data across orgs (option D) is also not a good solution, as it can incur additional costs and dependencies. It may also not be able to handle real-time or near-real-time data orchestration requirements.

The recommended course of action to appropriately integrate the new Opportunity engagement system with Salesforce is to bring the stakeholders together to discuss the appropriate data strategy moving forward. This is because there may be valuable data in both systems that need to be reconciled and harmonized, and the Master Data Management (MDM) strategy may need to be revised or updated to accommodate the new system. The other options are not recommended, as they may result in data loss, inconsistency, or duplication

 The Data Architect should consider the limitations on Org data storage and the Bulk API limitations on picklist fields when denormalizing the data model into a single Transaction object with a Branch Office picklist. The Org data storage limit is the total amount of data that can be stored in a Salesforce Org, and it depends on the edition and license type of the Org1. The Bulk API limit on picklist fields is the maximum number of values that can be imported or exported using the Bulk API, and it is 1,000 values per picklist field2. These limitations could affect the performance and scalability of the data model, and the Data Architect should plan accordingly.