Salesforce Data-Architect Exam With Confidence Using Practice Dumps

The three key steps that a data architect should take when merging data from multiple systems into Salesforce are:

Analyze each system’s data model and perform gap analysis. This step involves understanding the structure and meaning of the data in each system, identifying the common and unique data elements, and mapping the data fields between the systems. This step also involves assessing the quality and consistency of the data, and identifying any data cleansing or transformation needs.

Utilize an ETL tool to merge, transform, and de-duplicate data. This step involves using an ETL tool to connect to the source systems, extract the data, apply any data transformations or validations, and load the data into Salesforce. This step also involves applying de-duplication rules or algorithms to avoid creating duplicate records in Salesforce.

Work with stakeholders to define record and field survivorship rules. This step involves collaborating with the business users and owners of the data to determine which records and fields should be retained or overwritten in case of conflicts or discrepancies. This step also involves defining the criteria and logic for record and field survivorship, and implementing them in the ETL tool or in Salesforce.

Creating new fields to store additional values from all the systems is not a key step, but rather a possible outcome of the gap analysis. It may not be necessary or desirable to create new fields for every value from every system, as it may result in redundant or irrelevant data. Installing a 3rd party AppExchange tool to handle the merger is not a key step, but rather a possible option for choosing an ETL tool. It may not be the best option depending on the requirements, budget, and preferences of the organization.

When denormalizing a data model into a single object with a picklist field, the data architect should consider the Bulk API limitations on picklist fields and the standard list view in-line editing. The Bulk API has a limit of 1,000 distinct picklist values per file1, which could be an issue if there are more than 1,000 venues in the future. The standard list view in-line editing allows users to edit multiple records at once, which could introduce data quality issues if the venue picklist is not validated or restricted2. The other options are not relevant to denormalizing a data model.

The correct answer is A, D, and E. The data architect should consider these three factors when evaluating the need for Salesforce Connect: You want real-time access to the latest data from other systems, you have a large amount of data that you don’t want to copy into your Salesforce org, and you need to small amounts of external data at any one time. These factors indicate that Salesforce Connect is a suitable solution for creating a connected experience in Salesforce without importing inventory data from a cloud database. Salesforce Connect allows Salesforce to access external data via OData or custom adapters without storing it in Salesforce, which reduces storage costs and ensures data freshness. Salesforce Connect also supports pagination and caching to optimize performance when accessing small amounts of external data at any one time. Option B is incorrect because if you have a large amount of data and would like to copy subsets of it into Salesforce, you may not need Salesforce Connect but rather use other tools such as Data Loader or API integration. Option C is incorrect because if you need to expose data via a virtual private connection, you may not need Salesforce Connect but rather use other tools such as VPN or VPC peering.