diff --git a/content/docs/pipelines/pipelines-quickstart.md b/content/docs/pipelines/pipelines-quickstart.md
index ebf91e8aa5..0271c9c48e 100644
--- a/content/docs/pipelines/pipelines-quickstart.md
+++ b/content/docs/pipelines/pipelines-quickstart.md
@@ -161,7 +161,7 @@ Follow these steps to set up the necessary GCP services and run the sample:
 
 You can find the source code for the XGBoost training sample in the 
 [Kubeflow Pipelines 
-repo](https://github.com/kubeflow/pipelines/tree/master/samples/core/xgboost-spark).
+repo](https://github.com/kubeflow/pipelines/tree/master/samples/core/xgboost_training_cm).
 
 ## Clean up your GCP environment
 
diff --git a/content/docs/pipelines/sdk/output-viewer.md b/content/docs/pipelines/sdk/output-viewer.md
index 1333f369f1..145aaae7f8 100644
--- a/content/docs/pipelines/sdk/output-viewer.md
+++ b/content/docs/pipelines/sdk/output-viewer.md
@@ -438,7 +438,7 @@ Kubeflow Pipelines UI. For help getting started with the UI, follow the
 [Kubeflow Pipelines quickstart](/docs/pipelines/pipelines-quickstart/).
 
 The sample code is available in the [Kubeflow Pipelines samples 
-repo](https://github.com/kubeflow/pipelines/tree/master/samples/tfx). The
+repo](https://github.com/kubeflow/pipelines/tree/master/samples/core/tfx_cab_classification). The
 pipeline uses a number of prebuilt, reusable components, including:
 
 * The [Confusion Matrix