AP-226 · Question #80
Your data includes sensitive customer information. Which security mechanism protects data during migration?
The correct answer is B. Phased migration migrating specific data subsets in stages to minimize service interruption. Ensuring the security of sensitive customer data during migration requires a multi-layered approach. Data encryption both at rest and in transit, via SSL/TLS protocols, ensures the data is protected against unauthorized access during transfer. User permissions and field-level…
Question
Your data includes sensitive customer information. Which security mechanism protects data during migration?
Options
- AFull cutover migration transferring all data at once, followed by system downtime for
- BPhased migration migrating specific data subsets in stages to minimize service interruption.
- CIncremental migration continuously syncing updates from legacy systems to Salesforce for real-
- DPilot migration testing the process with a small data sample before large-scale migration
How the community answered
(20 responses)- A25% (5)
- B60% (12)
- C10% (2)
- D5% (1)
Explanation
Ensuring the security of sensitive customer data during migration requires a multi-layered approach. Data encryption both at rest and in transit, via SSL/TLS protocols, ensures the data is protected against unauthorized access during transfer. User permissions and field-level security within Salesforce restrict access to sensitive data. Data anonymization or pseudonymization techniques mask sensitive information, providing additional privacy. "D. All of the above, forming a multi- layered approach to data security during and after migration" offers comprehensive protection for sensitive data throughout the migration process. You need to migrate both active and historical customer dat time data consistency. A phased migration approach, where specific subsets of data (both active and historical customer data) are migrated in stages, balances efficiency and minimal disruption. This strategy allows continuous operation of both legacy and new systems during the transition phase, minimizing downtime and service interruptions. It also provides opportunities to address issues on a smaller scale before they affect the entire dataset.
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