Salesforce Certified Data Architecture Practice Test

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What should an architect consider to optimize data load time when migrating data between Salesforce instances post-acquisition?

  1. Pre-process the data, then use Data Loader with SOAP API to upsert with zip compression enabled.

  2. Directly leverage Salesforce-to-Salesforce functionality to load Case data.

  3. Load the data in multiple sets using Bulk API parallel processes.

  4. Utilize the Salesforce Org Migration Tool from the Setup Data Management menu.

The correct answer is: Pre-process the data, then use Data Loader with SOAP API to upsert with zip compression enabled.

An architect aiming to optimize data load time during a data migration process should consider the approach that allows for efficient handling of data volumes and minimizes the time taken for data operations. Choosing to pre-process the data and then utilizing Data Loader with the SOAP API for upserting data, while ensuring that zip compression is enabled, is a strategic method for several reasons. Pre-processing the data ensures that it is clean, organized, and structured appropriately before it reaches the actual loading phase. This preparation reduces the likelihood of errors and rework that can arise from loading unrefined data, which can slow down overall migration efforts. Furthermore, using the Data Loader leverages Salesforce's robust back-end capabilities to handle the data upserts efficiently. The SOAP API is well-suited for real-time operations, allowing for immediate execution of upsert processes. In addition, enabling zip compression during data transfer reduces the amount of data sent over the network, which can significantly decrease loading times, especially when dealing with large datasets. This optimization takes advantage of network bandwidth to enhance performance. Other methods such as leveraging Salesforce-to-Salesforce functionality may not provide the same level of control over the data being migrated or may not be ideal for high-volume data transfers. Similarly, while loading data in multiple

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