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Salesforce Certified Data Architect is a prestigious certification program that is designed to validate the skills and knowledge required to design and implement effective data solutions in the Salesforce platform. Salesforce Certified Data Architect certification program is ideal for professionals who are looking to advance their careers in the field of data architecture and management. The Salesforce Certified Data Architect certification is recognized globally and is highly valued by employers in the industry.

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Salesforce Certified Data Architect Sample Questions (Q57-Q62):

NEW QUESTION # 57
Universal Containers has a requirement to store more than 100 million records in salesforce and needs to create a custom big object to support this business requirement.
Which two tools should a data architect use to build custom object?

Answer: A,D

Explanation:
To build a custom big object to support storing more than 100 million records in Salesforce, a data architect should use Metadata API or Big Object in setup. Metadata API is an API that allows developers to create, retrieve, update, or delete metadata components in Salesforce programmatically. Big Object in setup is a user interface that allows admins to create big objects declaratively without writing code. Both tools can be used to define custom big objects and their fields, indexes, and relationships in Salesforce. Option A is incorrect because DX (Developer Experience) is a set of tools that allows developers to create and manage applications on Salesforce Platform, but it does not support creating big objects directly. Option D is incorrect because Object Manager in setup is a user interface that allows admins to create and manage standard and custom objects in Salesforce, but it does not support creating big objects declaratively.


NEW QUESTION # 58
North Trail Outfitters (NTO) operates a majority of its business from a central Salesforce org, NTO also owns several secondary orgs that the service, finance, and marketing teams work out of, At the moment, there is no integration between central and secondary orgs, leading to data-visibility issues.
Moving forward, NTO has identified that a hub-and-spoke model is the proper architect to manage its data, where the central org is the hub and the secondary orgs are the spokes.
Which tool should a data architect use to orchestrate data between the hub org and spoke orgs?

Answer: C

Explanation:
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.


NEW QUESTION # 59
Universal Containers (UC) is a major supplier of office supplies. Some products are produced by UC and some by other manufacturers. Recently, a number of customers have complained that product descriptions on the invoices do not match the descriptions in the online catalog and on some of the order confirmations (e.g.,
"ballpoint pen" in the catalog and "pen" on the invoice, and item color labels are inconsistent: "what vs.
"White" or "blk" vs. "Black"). All product data is consolidated in the company data warehouse and pushed to Salesforce to generate quotes and invoices. The online catalog and webshop is a Salesforce Customer Community solution. What is a correct technique UC should use to solve the data inconsistency?

Answer: B

Explanation:
A correct technique UC should use to solve the data inconsistency is to define a data taxonomy for product data and apply the taxonomy to the product data in the data warehouse. A data taxonomy is a hierarchical classification of data entities and attributes that defines their meaning, format, and relationships. A data taxonomy can help ensure consistency, accuracy, and completeness of product data across different systems and channels


NEW QUESTION # 60
An architect is planning on having different batches to load one million Opportunities into Salesforce using the Bulk API in parallel mode. What should be considered when loading the Opportunity records?

Answer: D


NEW QUESTION # 61
Which API should a data architect use if exporting 1million records from Salesforce?

Answer: C


NEW QUESTION # 62
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