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Add tutorial links for Durable text analysis and FFmpeg processing in file access options article
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articles/azure-functions/concept-file-access-options.md

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@@ -15,6 +15,8 @@ This article compares three ways to access files from Azure Functions: storage b
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Storage bindings and external databases work on all hosting plans. Storage mounts are supported only on [Flex Consumption](./flex-consumption-plan.md) and [Dedicated (App Service)](./dedicated-plan.md) plans.
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If you want to jump straight to working code, see the [Tutorial: Durable text analysis with a mounted Azure Files share](./durable/tutorial-durable-text-analysis-azure-files.md) for parallel file processing or [Tutorial: Process images by using FFmpeg on a mounted Azure Files share](./tutorial-ffmpeg-processing-azure-files.md) for hosting large binaries on a mount.
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[!INCLUDE [functions-azure-files-samples-note](../../includes/functions-azure-files-samples-note.md)]
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## File access options at a glance
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> **Use case:** You have 1,000 analysis tasks that all need to read from the same set of reference data files (for example, ML models, lookup tables, or corpus data).
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> [!NOTE]
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> For a complete walkthrough of this pattern, see [Tutorial: Durable text analysis with a mounted Azure Files share](./durable/tutorial-durable-text-analysis-azure-files.md).
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**The problem:** Without mounts, you have two suboptimal options:
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- **Package the reference files with your function**: This approach results in a huge deployment artifact, slow cold starts, and storage redundancy.
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> **Use case:** You need to run a large third-party binary (500+ MB) on every instance, but you don't want to package it with your function code. Such binaries could include ffmpeg, ImageMagick, and others.
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> [!NOTE]
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> For a complete walkthrough of this pattern, see [Tutorial: Process images by using FFmpeg on a mounted Azure Files share](./tutorial-ffmpeg-processing-azure-files.md).
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**The problem:**
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- Without mounts:

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