Update Challenge-03.md

068-AzureOpenAIApps/Student/Challenge-03.md

@@ -22,16 +22,22 @@ The goal of this challenge is to observe the extraction of the school district,
 
 The Citrus Bus application has a pipeline that can process all of the historical PDF and PNG files for these exam submissions and activity preferences stored in blob storage.
 
-There are 20 sample documents in the sub-folders under the **`/artifacts/contoso-education`** folder:
+There are 21 sample documents in the sub-folders under the **`/artifacts/contoso-education`** folder:
 
 - `/F01-Civics-Geography and Climate`
 - `/F02-Civics-Tourism and Economy`
 - `/F03-Civics-Government and Politics`
 - `/F04-Activity-Preferences`
 
-Each folder containers 5 samples you will use for training the custom classifier and extractor.
+Each folder contains 5 samples (except for `/F01-Civics-Geography and Climate` which has 6) that you will use for training the custom classifier and extractor.
+
+In Azure Blob Storage you should see a container called **`classifications`**. There should be a total of 21 samples from the 4 classes or categories inside the **`classifications`** container. They were also copied to these containers in Azure Blob Storage:
+
+- `f01-geo-climate`
+- `f02-tour-economy`
+- `f03-gov-politics`
+- `f04-activity-preferences`
 
-In Azure Blob Store you should see a container called **`classifications`** with 5 document samples. There should be a total of 20 samples from the 4 classes or categories inside the **`classifications`** container in Blob Store.
 
 At runtime in the automated data pipeline, the app will invoke the custom classifier from Azure Document Intelligence to recognize which document type it has encountered and then it will call the corresponding custom extractor model to parse the document and extract the relevant fields.