Merge pull request #5896 from Albertyang0/2025_07-Monthly-broken-links-fix-ay-100

prmerger-automator[bot] · web-flow · commit dc69e1e5d2a8 · 2025-07-21T18:39:31.000Z
2025_07 - Fix monthly broken links
diff --git a/articles/machine-learning/how-to-access-data-interactive.md b/articles/machine-learning/how-to-access-data-interactive.md
@@ -655,7 +655,7 @@ df.head()
 > [!TIP]
 > Pandas is not designed to handle large datasets. Pandas can only process data that can fit into the memory of the compute instance.
 >
-> For large datasets, we recommend use of Azure Machine Learning managed Spark. This provides the [PySpark Pandas API](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html).
+> For large datasets, we recommend use of Azure Machine Learning managed Spark. This provides the [PySpark Pandas API](https://spark.apache.org/docs/3.5.3/api/python/user_guide/pandas_on_spark/index.html).
 
 You might want to iterate quickly on a smaller subset of a large dataset before scaling up to a remote asynchronous job. `mltable` provides in-built functionality to get samples of large data using the [take_random_sample](/python/api/mltable/mltable.mltable.mltable#mltable-mltable-mltable-take-random-sample) method:
 

Original file line number	Diff line number	Diff line change
`@@ -655,7 +655,7 @@ df.head()`
`655`	`655`	`> [!TIP]`
`656`	`656`	`> Pandas is not designed to handle large datasets. Pandas can only process data that can fit into the memory of the compute instance.`
`657`	`657`	`>`
`658`		`-> For large datasets, we recommend use of Azure Machine Learning managed Spark. This provides the [PySpark Pandas API](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html).`
	`658`	`+> For large datasets, we recommend use of Azure Machine Learning managed Spark. This provides the [PySpark Pandas API](https://spark.apache.org/docs/3.5.3/api/python/user_guide/pandas_on_spark/index.html).`
`659`	`659`
`660`	`660`	You might want to iterate quickly on a smaller subset of a large dataset before scaling up to a remote asynchronous job. `mltable` provides in-built functionality to get samples of large data using the [take_random_sample](/python/api/mltable/mltable.mltable.mltable#mltable-mltable-mltable-take-random-sample) method:
`661`	`661`