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This is an unsafe or unsupported shortcut: Use an ordinary free-text field for every computed or enumerated value and assume users will keep formulas and naming consistent. It fails to address Dataverse column choices formulas and rollups. In this scenario: A warranty policy calculates expiry from purchase date but the team relies on a user-maintained text column with no formula. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Alternate keys and Dataverse migration integrity, rather than directly to Dataverse column choices formulas and rollups: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling.. In this scenario: A warranty policy calculates expiry from purchase date but the team relies on a user-maintained text column with no formula. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Dataverse owner teams access teams and column protection, rather than directly to Dataverse column choices formulas and rollups: Compare team type and membership, record sharing, role privileges, column security enablement and controlled employee-field access attempts.. In this scenario: A warranty policy calculates expiry from purchase date but the team relies on a user-maintained text column with no formula. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Dataverse record ownership and table security model, rather than directly to Dataverse column choices formulas and rollups: Inspect table ownership metadata, required per-record confidentiality, role access levels and sample users in each business unit.. In this scenario: A warranty policy calculates expiry from purchase date but the team relies on a user-maintained text column with no formula. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Dataverse owner teams access teams and column protection, rather than directly to Dataverse column choices formulas and rollups: Compare team type and membership, record sharing, role privileges, column security enablement and controlled employee-field access attempts.. In this scenario: A warranty policy calculates expiry from purchase date but the team relies on a user-maintained text column with no formula. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This is an unsafe or unsupported shortcut: Use an ordinary free-text field for every computed or enumerated value and assume users will keep formulas and naming consistent. It fails to address Dataverse column choices formulas and rollups. In this scenario: A warranty policy calculates expiry from purchase date but the team relies on a user-maintained text column with no formula. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Dataverse activity tables and timeline records, rather than directly to Dataverse column choices formulas and rollups: Users see appropriately related interaction history with expected activity fields and filtering on the relevant model-driven form.. In this scenario: A warranty policy calculates expiry from purchase date but the team relies on a user-maintained text column with no formula. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Virtual Dataverse tables for external records, rather than directly to Dataverse column choices formulas and rollups: The app retrieves current authorized source records with known limitations and without creating unintended competing master copies.. In this scenario: A warranty policy calculates expiry from purchase date but the team relies on a user-maintained text column with no formula. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This is an unsafe or unsupported shortcut: Use an ordinary free-text field for every computed or enumerated value and assume users will keep formulas and naming consistent. It fails to address Dataverse column choices formulas and rollups. In this scenario: A warranty policy calculates expiry from purchase date but the team relies on a user-maintained text column with no formula. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This is an unsafe or unsupported shortcut: Use an ordinary free-text field for every computed or enumerated value and assume users will keep formulas and naming consistent. It fails to address Dataverse column choices formulas and rollups. In this scenario: A service desk needs fixed statuses for triage and uses uncontrolled free-text values that differ across reports. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Dataverse cascading relationship behavior, rather than directly to Dataverse column choices formulas and rollups: Configure relationship behavior and individual cascade actions to match retention, assignment, share and delete requirements; test them on sample records.. In this scenario: A service desk needs fixed statuses for triage and uses uncontrolled free-text values that differ across reports. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Dataverse owner teams access teams and column protection, rather than directly to Dataverse column choices formulas and rollups: Choose an owner team, access team or Entra group team as appropriate, and apply column security profiles on eligible sensitive fields in addition to row access.. In this scenario: A service desk needs fixed statuses for triage and uses uncontrolled free-text values that differ across reports. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Dataverse record ownership and table security model, rather than directly to Dataverse column choices formulas and rollups: Table ownership influences available record-level security semantics and is selected when the Dataverse table is created, rather than being an ordinary later property change.. In this scenario: A service desk needs fixed statuses for triage and uses uncontrolled free-text values that differ across reports. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Dataverse owner teams access teams and column protection, rather than directly to Dataverse column choices formulas and rollups: Owner teams can own records and carry roles, access teams grant shared record access, and column security adds protection only after underlying record access exists.. In this scenario: A service desk needs fixed statuses for triage and uses uncontrolled free-text values that differ across reports. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This is an unsafe or unsupported shortcut: Use an ordinary free-text field for every computed or enumerated value and assume users will keep formulas and naming consistent. It fails to address Dataverse column choices formulas and rollups. In this scenario: A service desk needs fixed statuses for triage and uses uncontrolled free-text values that differ across reports. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Dataverse search scope and indexing, rather than directly to Dataverse column choices formulas and rollups: Inspect search availability, table search settings, searchable field configuration and query outcomes after indexing has caught up.. In this scenario: A service desk needs fixed statuses for triage and uses uncontrolled free-text values that differ across reports. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This is an unsafe or unsupported shortcut: Use an ordinary free-text field for every computed or enumerated value and assume users will keep formulas and naming consistent. It fails to address Dataverse column choices formulas and rollups. In this scenario: A service desk needs fixed statuses for triage and uses uncontrolled free-text values that differ across reports. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Alternate keys and Dataverse migration integrity, rather than directly to Dataverse column choices formulas and rollups: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration.. In this scenario: A service desk needs fixed statuses for triage and uses uncontrolled free-text values that differ across reports. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Dataverse cascading relationship behavior, rather than directly to Dataverse column choices formulas and rollups: Child records are retained, restricted, reassigned or deleted exactly as the approved lifecycle and audit policy specifies.. In this scenario: A service desk needs fixed statuses for triage and uses uncontrolled free-text values that differ across reports. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Dataverse owner teams access teams and column protection, rather than directly to Dataverse column choices formulas and rollups: Team access and sensitive-column visibility match least-privilege test personas even as authorized group memberships change.. In this scenario: A service desk needs fixed statuses for triage and uses uncontrolled free-text values that differ across reports. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This is an unsafe or unsupported shortcut: Use an ordinary free-text field for every computed or enumerated value and assume users will keep formulas and naming consistent. It fails to address Dataverse column choices formulas and rollups. In this scenario: A service desk needs fixed statuses for triage and uses uncontrolled free-text values that differ across reports. The relevant technical behavior is: Choices standardize enumerated values; formula columns calculate supported expressions; rollups aggregate related records with update behavior that differs from immediate manual recalculation. The appropriate implementation is: Choose column types appropriately and evaluate supported choice, formula and rollup semantics for derived or aggregated information. Independent validation: Inspect the column type, expression or rollup definition, refresh behavior, related records and displayed values after sample changes. Accept only when: The requested statuses and calculations are consistent, within supported feature limits, and match expected values after appropriate refresh.

This response applies to the different subject of Dataverse auditing configuration and traceability, rather than directly to Alternate keys and Dataverse migration integrity: Enable appropriate environment, table and column auditing and review authorized access, retention and storage effects.. In this scenario: A legacy CRM import includes stable external customer identifiers, but rerunning the migration creates duplicate rows. The relevant technical behavior is: Alternate keys support uniqueness and identification for integration scenarios where the Dataverse row ID is not the source system key. The appropriate implementation is: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling. Independent validation: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration. Accept only when: A repeated import updates the intended rows without creating additional duplicates or merging unrelated business records.

This is an unsafe or unsupported shortcut: Match legacy records only by a nonunique display label and insert a new row on every scheduled retry. It fails to address Alternate keys and Dataverse migration integrity. In this scenario: A legacy CRM import includes stable external customer identifiers, but rerunning the migration creates duplicate rows. The relevant technical behavior is: Alternate keys support uniqueness and identification for integration scenarios where the Dataverse row ID is not the source system key. The appropriate implementation is: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling. Independent validation: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration. Accept only when: A repeated import updates the intended rows without creating additional duplicates or merging unrelated business records.

This response applies to the different subject of Dataverse record ownership and table security model, rather than directly to Alternate keys and Dataverse migration integrity: Select the appropriate organization-owned or user/team-owned table model during design and align security roles with the intended record-access scope.. In this scenario: A legacy CRM import includes stable external customer identifiers, but rerunning the migration creates duplicate rows. The relevant technical behavior is: Alternate keys support uniqueness and identification for integration scenarios where the Dataverse row ID is not the source system key. The appropriate implementation is: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling. Independent validation: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration. Accept only when: A repeated import updates the intended rows without creating additional duplicates or merging unrelated business records.

This response applies to the different subject of Dataverse activity tables and timeline records, rather than directly to Alternate keys and Dataverse migration integrity: Dataverse activity records have specialized activity semantics that can support interaction histories and timeline experiences across related business records.. In this scenario: A legacy CRM import includes stable external customer identifiers, but rerunning the migration creates duplicate rows. The relevant technical behavior is: Alternate keys support uniqueness and identification for integration scenarios where the Dataverse row ID is not the source system key. The appropriate implementation is: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling. Independent validation: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration. Accept only when: A repeated import updates the intended rows without creating additional duplicates or merging unrelated business records.

This response applies to the different subject of Virtual Dataverse tables for external records, rather than directly to Alternate keys and Dataverse migration integrity: Virtual tables can surface externally stored data through provider capabilities; their data is not automatically stored or supported exactly like normal Dataverse rows.. In this scenario: A legacy CRM import includes stable external customer identifiers, but rerunning the migration creates duplicate rows. The relevant technical behavior is: Alternate keys support uniqueness and identification for integration scenarios where the Dataverse row ID is not the source system key. The appropriate implementation is: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling. Independent validation: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration. Accept only when: A repeated import updates the intended rows without creating additional duplicates or merging unrelated business records.

This is an unsafe or unsupported shortcut: Match legacy records only by a nonunique display label and insert a new row on every scheduled retry. It fails to address Alternate keys and Dataverse migration integrity. In this scenario: A legacy CRM import includes stable external customer identifiers, but rerunning the migration creates duplicate rows. The relevant technical behavior is: Alternate keys support uniqueness and identification for integration scenarios where the Dataverse row ID is not the source system key. The appropriate implementation is: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling. Independent validation: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration. Accept only when: A repeated import updates the intended rows without creating additional duplicates or merging unrelated business records.

This response applies to the different subject of Dataverse cascading relationship behavior, rather than directly to Alternate keys and Dataverse migration integrity: Inspect relationship cascade settings and compare before-and-after parent and child records during controlled assign and delete tests.. In this scenario: A legacy CRM import includes stable external customer identifiers, but rerunning the migration creates duplicate rows. The relevant technical behavior is: Alternate keys support uniqueness and identification for integration scenarios where the Dataverse row ID is not the source system key. The appropriate implementation is: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling. Independent validation: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration. Accept only when: A repeated import updates the intended rows without creating additional duplicates or merging unrelated business records.

This response applies to the different subject of Dataverse activity tables and timeline records, rather than directly to Alternate keys and Dataverse migration integrity: Inspect activity table metadata, regarding relationships, timeline settings and test appointment or task records on target forms.. In this scenario: A legacy CRM import includes stable external customer identifiers, but rerunning the migration creates duplicate rows. The relevant technical behavior is: Alternate keys support uniqueness and identification for integration scenarios where the Dataverse row ID is not the source system key. The appropriate implementation is: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling. Independent validation: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration. Accept only when: A repeated import updates the intended rows without creating additional duplicates or merging unrelated business records.

This is an unsafe or unsupported shortcut: Match legacy records only by a nonunique display label and insert a new row on every scheduled retry. It fails to address Alternate keys and Dataverse migration integrity. In this scenario: A legacy CRM import includes stable external customer identifiers, but rerunning the migration creates duplicate rows. The relevant technical behavior is: Alternate keys support uniqueness and identification for integration scenarios where the Dataverse row ID is not the source system key. The appropriate implementation is: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling. Independent validation: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration. Accept only when: A repeated import updates the intended rows without creating additional duplicates or merging unrelated business records.

This is an unsafe or unsupported shortcut: Match legacy records only by a nonunique display label and insert a new row on every scheduled retry. It fails to address Alternate keys and Dataverse migration integrity. In this scenario: A legacy CRM import includes stable external customer identifiers, but rerunning the migration creates duplicate rows. The relevant technical behavior is: Alternate keys support uniqueness and identification for integration scenarios where the Dataverse row ID is not the source system key. The appropriate implementation is: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling. Independent validation: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration. Accept only when: A repeated import updates the intended rows without creating additional duplicates or merging unrelated business records.

This response applies to the different subject of Dataverse auditing configuration and traceability, rather than directly to Alternate keys and Dataverse migration integrity: Enable appropriate environment, table and column auditing and review authorized access, retention and storage effects.. In this scenario: A legacy CRM import includes stable external customer identifiers, but rerunning the migration creates duplicate rows. The relevant technical behavior is: Alternate keys support uniqueness and identification for integration scenarios where the Dataverse row ID is not the source system key. The appropriate implementation is: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling. Independent validation: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration. Accept only when: A repeated import updates the intended rows without creating additional duplicates or merging unrelated business records.

This response applies to the different subject of Dataverse record ownership and table security model, rather than directly to Alternate keys and Dataverse migration integrity: Inspect table ownership metadata, required per-record confidentiality, role access levels and sample users in each business unit.. In this scenario: A legacy CRM import includes stable external customer identifiers, but rerunning the migration creates duplicate rows. The relevant technical behavior is: Alternate keys support uniqueness and identification for integration scenarios where the Dataverse row ID is not the source system key. The appropriate implementation is: Define suitable alternate keys for stable business identifiers and use key-aware import or upsert with tested collision handling. Independent validation: Review key status, source key uniqueness, upsert mappings and row counts before and after a repeated sample migration. Accept only when: A repeated import updates the intended rows without creating additional duplicates or merging unrelated business records.