Migration validation must establish that the complete migrated population is materially consistent with the source system, not merely confirm that a small sample of rows appears correct.

Creating custom reports that aggregate Sales Agreement values provides a controlled reconciliation mechanism. Administrators can compare record counts, planned quantities, revenues, actual amounts, and other meaningful totals between Salesforce and the legacy platform. Aggregate reconciliation is particularly useful because it can expose missing groups of records, incorrect mappings, truncated values, or numerical transformation issues that random sampling may not detect.

Salesforce's current Agentforce Manufacturing Professional exam outline specifically includes the ability to load and validate data under the Validate and Deploy domain. Salesforce also provides Manufacturing-specific CSV tooling that supports insert, update, and upsert operations along with validation and import logs, reinforcing the requirement to validate imported datasets systematically.

Randomly checking selected source rows provides useful spot validation but cannot demonstrate overall completeness. Exporting records with Data Loader and manually comparing CSV files is also less scalable and less effective for business-level reconciliation than aggregated reporting.

The preferred test is therefore to build reconciliation reports and compare relevant totals and counts across the two systems.

Study Guide references/topics: Validate and Deploy — Data Migration; Data Validation; Reconciliation; Sales Agreement Migration; Reporting.

Because the Sales Agreements use Product as the product level and calculate actuals Automatically from Direct Orders, the historical migration must include the records needed to establish customer, product, and order relationships.

Accounts identify the customers associated with the Sales Agreements. Products identify the items represented in the product-level agreements. Orders and Order Lines provide the historical transactional quantities from which Sales Agreement actuals can be calculated after the records are correctly associated. Salesforce's Manufacturing data model places Sales Agreement, Product, Order, and Order Product information within the Sales Agreement actuals architecture.

Salesforce explicitly states that with Automatically from direct orders, actual quantities are derived from active Orders that reference the relevant Sales Agreement.

Invoices are not the source used by the standard Direct Orders actuals mode. They may be relevant to financial systems or custom actuals processing, but they are not required by this configuration.

Opportunities represent prospective sales pipeline and likewise are not required for the direct-order actuals calculation.

Therefore, the historical data design must account for Accounts, Products, Orders, and Order Lines before activating the Sales Agreements.

Study Guide references/topics: Validate and Deploy — Historical Data Migration; Sales Agreements; Direct Orders; Accounts; Products; Order Products; Actuals Calculation.

Sales Agreement data must be loaded according to the parent-child dependency of the Manufacturing Cloud data model. The correct sequence is therefore Sales Agreement first, followed by Sales Agreement Product, and finally Sales Agreement Product Schedule.

The Sales Agreement is the parent commercial record representing the long-term relationship between the buyer and seller. A Sales Agreement Product belongs to that agreement and stores quantity, revenue, and pricing information for a specific product or product category across the agreement's duration. The Sales Agreement Product Schedule is subordinate to the product record and stores the same class of commercial metrics for an individual schedule period.

Consequently, a Sales Agreement Product cannot be represented correctly until its parent Sales Agreement exists, and a schedule should not be inserted until the corresponding Sales Agreement Product exists. This parent-before-child loading strategy preserves lookup relationships and prevents orphaned or unresolved records.

The operation is an initial data upload, so the records must be inserted, not updated. Updates apply when target records already exist and their values need modification.

This question differs from the broader migration-order question involving Accounts and Orders because it specifically validates the internal Sales Agreement object hierarchy.

Study Guide references/topics: Validate and Deploy — Data Migration; Sales Agreement Data Model; Parent-Child Relationships; Sales Agreement Products; Product Schedules.

Field-Level Security is used to differentiate which forecast dimensions and measures individual user groups can view or edit.

Salesforce explicitly recommends configuring Field-Level Security on the Advanced Account Forecast context/set-use object and the forecast fact object to provide different user populations with separate levels of access to forecast data.

For example, a regional sales manager may be allowed to view and edit a regional adjustment measure while an account manager has read-only access—or no access—to that measure. Salesforce's Advanced Account Forecasting training provides this exact type of implementation example, where regional managers and account managers receive different visibility and editing rights to quantity, revenue, and adjustment measures.

Record-level sharing determines whether a user can access the Forecast Partner or Fact record itself. Field-Level Security operates at a more granular level and determines whether individual fields on those accessible records are visible or editable.

Therefore, the objective is not merely to grant access to the records; it is to establish separate visibility levels for forecast data.

Study Guide references/topics: Configure and Build — Advanced Account Forecasting Security; Field-Level Security; Forecast Facts; Forecast Measures; Data Visibility.