Free Practice Questions for Salesforce Data-Architect Exam

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.

 Option C is the best answer because reviewing projected data storage needs is an important step in designing a field-level audit and recovery plan1. You need to estimate how much data storage you will need in the future and plan accordingly. Option A is not correct because reducing data storage by purging old data may not be sufficient or desirable for backup purposes. Option B is not correct because implementing an AppExchange package may not be customized or compatible with your org’s requirements. Option D is not correct because scheduling a weekly export file may not be frequent or granular enough for field-level audit and recovery.

Implicit sharing will automatically handle this sharing requirement with standard functionality, as it grants read-only access to parent accounts when users have access to child opportunities. This is also known as account-opportunity sharing3. Creating a criteria-based sharing rule (option A) or an owner-based sharing rule (option D) is not necessary, as they are used to grant additional access based on record criteria or ownership. Adding appropriate users to the account team (option C) is also not required, as it is used to grant access to specific users or groups for individual accounts.

 Skinny tables are custom tables that contain frequently used fields from a standard or custom object. They are used to improve performance by reducing the number of database joins required for queries. Skinny tables have the following characteristics1:

They do not include soft-deleted records, which means they only contain active records and save space.

They avoid resource intensive joins by storing data from multiple objects in one table, which reduces the query time and complexity.

Their tables are kept in sync with their source tables when the source tables are modified, which ensures data consistency and accuracy.

Exploring external data sources in Salesforce to build 360 view of customer is the best strategy to satisfy the requirement, as it allows real-time access to data from other systems without storing it in Salesforce3. Using a customer data mart may not provide real-time information or may require additional integration efforts. Migrating customer activities from all 4 systems into Salesforce may exceed the storage limits or cause data quality issues. Periodically uploading summary information in Salesforce may not provide current or detailed information.

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.

To allow only the owner of a contact record to delete that contact, the data architect should create a “before delete” trigger to check if the current user is not the owner. The trigger can use the UserInfo.getUserId() method to get the current user’s ID and compare it with the OwnerId field of the contact record. If they are not equal, the trigger can add an error to the record and prevent it from being deleted. The other options are not suitable for meeting the requirements, as they would either restrict the edit access or delete access for all users, regardless of ownership.

 Making sure the query does not contain NULL in any filter criteria can avoid full table scans and leverage indexes more efficiently. Queries with NULL filters are not selective and can cause performance issues. Creating custom indexes on the fields used in the filter criteria can also enhance the query performance by reducing the number of records to scan.

Removing custom indexes on the data being loaded will prevent unnecessary index maintenance and improve the data load speed. Deferring sharing calculations of the Salesforce Org will avoid frequent sharing rule evaluations and reduce the load time. Deactivating approval processes and workflow rules will prevent triggering any automation logic that might slow down or fail the data load.

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.