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Author: Stacyrch140

articles/machine-learning/tutorial-feature-store-domain-specific-language.md

@@ -81,7 +81,7 @@ Before you proceed with this tutorial, make sure that you cover these prerequisi
       1. Select **Apache Spark version** as `Spark version 3.3`
       1. Optionally, increase the **Session timeout** (idle time) if you want to avoid frequent restarts of the serverless Spark session
       1. Under **Configuration settings**, define *Property* `spark.jars.packages` and *Value* `com.microsoft.azure:azureml-fs-scala-impl:1.0.4`
-         :::image type="content" source="./media/tutorial-feature-store-domain-specific-language/dsl-spark-jars-property.png" lightbox="./media/tutorial-feature-store-domain-specific-language/dsl-spark-jars-property.png" alt-text="This screenshot shows the Spark session property for a package that contains the jar file used by managed feature store domain-specific language":::
+         :::image type="content" source="./media/tutorial-feature-store-domain-specific-language/dsl-spark-jars-property.png" lightbox="./media/tutorial-feature-store-domain-specific-language/dsl-spark-jars-property.png" alt-text="This screenshot shows the Spark session property for a package that contains the jar file used by managed feature store domain-specific language.":::
       1. Select **Python packages**
       1. Select **Upload conda file**
       1. Select the `conda.yml` you downloaded on your local device
@@ -338,4 +338,4 @@ The [fifth tutorial in the series](./tutorial-develop-feature-set-with-custom-so
 ## Next steps
 
 * [Part 2: Experiment and train models using features](./tutorial-experiment-train-models-using-features.md)
-* [Part 3: Enable recurrent materialization and run batch inference](./tutorial-enable-recurrent-materialization-run-batch-inference.md)
+* [Part 3: Enable recurrent materialization and run batch inference](./tutorial-enable-recurrent-materialization-run-batch-inference.md)