Salesforce Data-Architect - Salesforce Certified Data Architect (SP25)

According to the exam guide, one of the objectives is to "describe the use cases and considerations for deferring sharing calculations"1. This implies that option A is the correct way to load large amounts of data into Salesforce without affecting performance and data integrity. Deferring sharing calculations allows the data to be loaded first and then the sharing rules to be applied later2. Option B is not correct because workflows are not recommended for calculating summary values, as they can cause performance issues and data skew3. Option C is not correct because triggers are also not recommended for calculating summary values, as they can cause governor limit errors and data inconsistency. Option D is not correct because external IDs are used to link records from different systems, not to improve data loading performance.

Contacting Salesforce to enable Defer Sharing Rules is the feature that would ensure that performance, when moving Sales Reps between regions, remains adequate while meeting existing requirements. Defer Sharing Rules allows you to defer sharing rule recalculation when you make changes to public groups or roles that are used in sharing rules. This can improve performance and avoid locking issues when you move users between groups or roles2. Implementing data archiving for old Shipment records will not help with the performance issue related to sharing rules. Contacting Salesforce to create Skinny tables on Shipment will not help with the performance issue related to sharing rules. Configuring shipment OWD to Public Read/Write will not meet the existing requirements of having a Private sharing model.

 Setting Customer-Reference_c as an External ID (unique) is the recommended solution to ensure that support agents using global search and reports can quickly find shipment records. This is because external ID fields are indexed and can be used as filters in reports and list views4. Making the field unique also ensures that there are no duplicate values that could confuse the search results.

 Data storage limits and API request limits are two important factors that affect the data integration and storage in Salesforce. Data storage limits determine how much data can be stored in Salesforce, and API request limits determine how many API calls can be made to Salesforce in a 24-hour period. Both of these limits depend on the edition and license type of the Salesforce org. Workflow rule limits and webservice callout limits are not directly related to data integration and storage, but rather to business logic and external services.

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

The three considerations needed prior to a SF Connect implementation are to develop an object relationship strategy, identify the external tables to sync into external objects, and assess whether the external data source is reachable via an ODATA endpoint. SF Connect is a feature that allows integrating external data sources with Salesforce using external objects. External objects are similar to custom objects, but they store metadata only and not data. They enable on-demand access to external data via standard Salesforce APIs and user interfaces. To implement SF Connect, a data architect needs to consider how the external objects will relate to other objects in Salesforce, which external tables will be exposed as external objects, and whether the external data source supports ODATA protocol for data access.

Creating a custom object to store a copy of the record when changed and creating a custom object to store the previous and new field values are two possible actions that an architect can perform to meet the requirement of tracking field history on 100 Opportunity fields. A custom object can store more fields and records than the standard field history tracking feature, which has a limit of 20 fields per object and 18 or 24 months of data retention. A custom object can also be used for reporting and analysis of field history data. The other options are not feasible or effective for meeting the requirement