Data-Architect Exam Dumps - Salesforce Data-Architect Questions and Answers

According to the Using Mashups article on Salesforce Developers, one of the techniques for deploying large data volumes is to use mashups to display historical records in Salesforce. The article states that “Mashups are a way to display data from an external system within a Salesforce page without copying or synchronizing the data. Mashups use a combination of Visualforce, Apex callouts, and JavaScript code that runs in the browser. Mashups are useful when you want to display large amounts of read-only data that is stored outside of Salesforce.” Therefore, a data architect should recommend this solution to meet the business requirement of UC.

Creating a unique global customer ID for each customer and storing that in all systems for referential identity (option A), investing in a data duplicate tool to de-dupe and merge duplicate records across all systems (option C), and creating a customer master database external to Salesforce as a system of truth and syncing the customer data with all systems (option E) are the three options that a data architect should perform to resolve the issues with the customer data. Option A ensures that each customer can be uniquely identified across different systems, option C eliminates duplicate records and improves data quality, and option E provides a consistent and reliable source of customer data for all systems. Using Salesforce CDC to sync customer data across all systems (option B) is not a good option, as it does not address the duplication or inconsistency issues. Duplicating customer records across the system and providing a two-way sync of data between the systems (option D) is also not a good option, as it may create more confusion and conflicts with customer data.

According to the Salesforce documentation2, duplicate leads are leads that have the same or similar information as other leads in Salesforce. Duplicate leads can cause poor customer experience, higher costs, and inaccurate reporting. To mitigate the issues caused by duplicate leads, some of the recommended practices are:

Standardize lead information across all channels (option B). This means using consistent formats, values, and fields for capturing lead data from different sources, such as web forms, email campaigns, or third-party vendors. This can help reduce data quality issues and make it easier to identify and prevent duplicate leads.

Implement a third-party solution to clean and enrich lead data (option D). This means using an external service or tool that can validate, correct, update, and enhance lead data before or after importing it into Salesforce. This can help improve data quality and accuracy, and reduce duplicate leads.

Implement a de-duplication strategy to prevent duplicate leads (option E). This means using Salesforce features or custom solutions that can detect and block duplicate leads from being created or imported into Salesforce. For example, using Data.com Duplicate Management3, which allows defining matching rules and duplicate rules for leads and other objects.

Building a process to manually search and merge duplicates (option A) is not a good practice, as it can be time-consuming, error-prone, and inefficient. Building a custom solution to identify and merge duplicate leads (option C) is also not a good practice, as it can be complex, costly, and difficult to maintain. It is better to use existing Salesforce features or third-party solutions that can handle duplicate leads more effectively.

The data classification feature allows customers to classify their data in Salesforce based on two policies:

Data sensitivity level: This policy defines how sensitive the data is and what level of protection it requires. For example, high sensitivity data may require encryption or masking.

Compliance categorization policy: This policy defines how the data is regulated by various laws and standards. For example, GDPR or PCI DSS.

Cleaning data before importing to Salesforce and using Salesforce matching and duplicate rules are two design considerations that an architect should take to minimize data duplication when migrating legacy account data from two aging systems into Salesforce. Cleaning data before importing involves removing or correcting any inaccurate, incomplete, or inconsistent data from the source systems, as well as identifying and resolving any potential duplicates. This ensures that only high-quality and unique data is imported to Salesforce. Using Salesforce matching and duplicate rules allows the architect to define how Salesforce identifies duplicate records during import and how users can handle them. This prevents or reduces the creation of duplicate records in Salesforce and improves data quality. The other options are not effective or recommended for minimizing data duplication.

Creating an account plan object with a lookup relationship to Account with validation rules to enforce the Account association can help UC meet their requirement. A lookup relationship allows different accessibility requirements for the account plan object and the account object, as well as different ownership and sharing settings. A validation rule can ensure that each account plan has an account associated with it.

According to the official Salesforce guide1, big objects are designed to store and manage massive data volumes within Salesforce without affecting performance. They can be queried by using Async SOQL or standard SOQL, and they can be accessed by using Apex, Visualforce, Lightning components, or APIs. Big objects are ideal for storing data that is used for analytics or reporting purposes, such as the reward program data. Option A is the correct answer because it allows NTO to create a custom big object in Salesforce that can store the reward program data and make it available for consumption by Einstein Analytics and Discovery. Option B is incorrect because Einstein cannot connect directly to the point of sales system to capture the reward program data. Option C is incorrect because Einstein Analytics does not support creating big objects. Option D is incorrect because custom objects are not suitable for storing large volumes of data.

To recommend a clean migration, the data architect should set up a staging database, and define external IDs to merge, clean duplicate data, and load into Salesforce. This will allow the data architect to consolidate and deduplicate the data from two legacy systems before importing it into Salesforce using external IDs as unique identifiers. Option A is incorrect because defining external IDs for an object, migrating second database to first database, and loading into Salesforce will not ensure that the data is clean and duplicate-free. Option B is incorrect because defining duplicate rules in Salesforce, and loading data into Salesforce from both databases will not prevent the duplicate data from being imported into Salesforce. Option D is incorrect because defining external IDs for an object, inserting data from one database, and using upsert for a second database will not handle the duplicate data from both databases.

The correct answer is A, global unique customer number. A global unique customer number is a data element that can uniquely identify each customer across different systems. It can be used as a key to link customer records from different sources and enable reporting across these systems. Email address, phone number, and full name are not reliable or consistent identifiers for customers, as they can change over time or be shared by multiple customers.

A master-detail field on VTOTime__c to Project__c is the data relationship that the data architect should use to support the requirement when creating the custom VTOTime__c object. A master-detail relationship creates a parent-child relationship between two objects, where the master record controls certain behaviors of the detail record, such as security, ownership, deletion, and roll-up summary fields. By using a master-detail field on VTOTime__c to Project__c, you can ensure that the ability to view or edit the VTOTime__c object is the same as the Project__c record, and that managers can see the total hours volunteered while viewing the Project__c record using a roll-up summary field.