When performing an on-demand data refresh, what issue should you consider?

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Multiple Choice

When performing an on-demand data refresh, what issue should you consider?

Explanation:
When you refresh on demand, you’re forcing the system to pull in fresh data from the sources, which can change what you’re seeing in the moment. The main idea to watch for is a potential temporary mismatch or inconsistency as the dataset in memory or in the UI gets updated. If you’ve been working with data, running calculations, or editing records, the refresh can alter values, filters, or results mid-session, leading to transient discrepancies until everything settles with the new data. This isn’t about guaranteeing a flawless, issue-free moment; there can be brief changes as the latest data replaces what was loaded. It also isn’t a guarantee that all caches will be cleared or that every data source will reload in full—the exact behavior depends on how the refresh is implemented. So, plan refreshes with that transient impact in mind, perhaps during quieter periods or with awareness that results may briefly change.

When you refresh on demand, you’re forcing the system to pull in fresh data from the sources, which can change what you’re seeing in the moment. The main idea to watch for is a potential temporary mismatch or inconsistency as the dataset in memory or in the UI gets updated. If you’ve been working with data, running calculations, or editing records, the refresh can alter values, filters, or results mid-session, leading to transient discrepancies until everything settles with the new data.

This isn’t about guaranteeing a flawless, issue-free moment; there can be brief changes as the latest data replaces what was loaded. It also isn’t a guarantee that all caches will be cleared or that every data source will reload in full—the exact behavior depends on how the refresh is implemented. So, plan refreshes with that transient impact in mind, perhaps during quieter periods or with awareness that results may briefly change.

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