Given a Customer Scenario, Recommend an Optimal Data Migration Plan Taking into Account Parallelism, Managing Locks, and Handling Sharing Rules. DATA-ARCHITECT Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   A data architect is planning a large data migration into Salesforce. Which approach best leverages parallel processing while minimizing the risk of hitting platform limits?

   • AUse a single batch job with a small batch size to avoid any risk of hitting limits.
   • BUse the Data Import Wizard for all records because it automatically handles parallelism and limits.
   • CRun multiple batch jobs in parallel, each with a moderate batch size, while monitoring API and concurrency limits.
   • DRun all batch jobs concurrently with the highest possible batch size to finish as quickly as possible.
   Reveal Solution Discussion Select an answer first
2. 7expert · hard

   A customer is migrating 20 million records across 5 objects. They have a 24-hour window. They want to use parallel processing to speed up the migration, but they are concerned about lock contention and API limits. They also need to ensure that sharing rules are correctly applied after migration. Which plan is most optimal?

   • ALoad all objects in parallel but disable sharing rules until after the migration.
   • BLoad all objects in parallel but use a batch size of 10,000 to reduce API calls.
   • CLoad all objects in parallel with maximum concurrency, then create sharing rules after the load.
   • DLoad objects sequentially, but use parallel batches within each object, and create sharing rules before the load.
   Reveal Solution Discussion Select an answer first
4. 8application · medium

   A company is migrating 2 million Account and 5 million Contact records into Salesforce. The data volume is large, and the migration window is tight. The team plans to use Data Loader with parallel batch jobs. What should the migration lead do FIRST to ensure the parallel jobs do not cause lock contention or hit API limits?

   • AUse a moderate number of parallel batches (e.g., 4-6) and stagger their start times, monitoring API usage and lock errors.
   • BSet up a batch size of 200 records and run multiple batches sequentially for each object, one object at a time.
   • CUse a single batch with a large batch size (e.g., 10,000 records) to minimize the number of API calls.
   • DRun all batches for Account and Contact simultaneously with maximum parallelism to finish quickly.
   Reveal Solution Discussion Select an answer first
5. 9application · medium

   A migration team is using Data Loader to load 1 million records. They want to use parallel batches to speed up the process. They are concerned about hitting the API request limit. What is the best way to monitor and adjust parallelism?

   • AMonitor the 'API Usage' section in the Salesforce Setup and adjust the number of parallel batches accordingly.
   • BUse the 'Bulk Data Load' feature in Data Loader, which automatically manages parallelism.
   • CUse the Salesforce 'System Overview' page to monitor API usage in real-time.
   • DUse a third-party tool that automatically throttles API calls.
   Reveal Solution Discussion Select an answer first
6. 10application · medium

   During a data migration, a team is loading records into a custom object that has roll-up summary fields and lookup relationships to Account. They notice that some batches fail with 'UNABLE_TO_LOCK_ROW' errors. What is the most effective way to reduce these lock errors?

   • ARun the migration during off-peak hours to reduce concurrent user activity.
   • BLoad records in smaller batches and avoid updating parent records in the same batch as child records.
   • CDisable the roll-up summary fields temporarily during the migration.
   • DIncrease the batch size to reduce the number of batches and API calls.
   Reveal Solution Discussion Select an answer first