diff --git a/dataproc/python-api-walkthrough.md b/dataproc/python-api-walkthrough.md
index f64d7528601..1a8d436f720 100644
--- a/dataproc/python-api-walkthrough.md
+++ b/dataproc/python-api-walkthrough.md
@@ -1,4 +1,4 @@
-# Use the Python Client Library to call Cloud Dataproc APIs
+# Use the Python Client Library to call Dataproc APIs
 
 Estimated completion time: <walkthrough-tutorial-duration duration="5"></walkthrough-tutorial-duration>
 
@@ -7,13 +7,13 @@ Estimated completion time: <walkthrough-tutorial-duration duration="5"></walkthr
 This [Cloud Shell](https://cloud.google.com/shell/docs/) walkthrough leads you
 through the steps to use the
 [Google Cloud Client Libraries for Python](https://googleapis.github.io/google-cloud-python/latest/dataproc/index.html)
-to programmatically interact with [Cloud Dataproc](https://cloud.google.com/dataproc/docs/).
+to programmatically interact with [Dataproc](https://cloud.google.com/dataproc/docs/).
 
 As you follow this walkthrough, you run Python code that calls
-[Cloud Dataproc gRPC APIs](https://cloud.google.com/dataproc/docs/reference/rpc/)
+[Dataproc gRPC APIs](https://cloud.google.com/dataproc/docs/reference/rpc/)
 to:
 
-* create a Cloud Dataproc cluster
+* create a Dataproc cluster
 * submit a small PySpark word sort job to run on the cluster
 * get job status
 * tear down the cluster after job completion
@@ -24,7 +24,7 @@ The `submit_job_to_cluster.py file` used in this walkthrough is opened in the
 Cloud Shell editor when you launch the walkthrough. You can view
 the code as your follow the walkthrough steps.
 
-**For more information**: See [Cloud Dataproc&rarr;Use the Python Client Library](https://cloud.google.com/dataproc/docs/tutorials/python-library-example) for
+**For more information**: See [Dataproc&rarr;Use the Python Client Library](https://cloud.google.com/dataproc/docs/tutorials/python-library-example) for
 an explanation of how the code works.
 
 **To reload this walkthrough:** Run the following command from the
@@ -41,8 +41,13 @@ an explanation of how the code works.
 1. Create or select a Google Cloud Platform project to use for this tutorial.
     * <walkthrough-project-billing-setup permissions=""></walkthrough-project-billing-setup>
 
-1. Enable the Cloud Dataproc, Compute Engine, and Cloud Storage APIs in your project.
-    * <walkthrough-enable-apis apis="dataproc,compute_component,storage-component.googleapis.com"></walkthrough-enable-apis>
+1. Click the link below to enable the Dataproc, Compute Engine, and Cloud Storage APIs
+   in a separate GCP console tab in your browser.
+    
+   **Note:** After you select your project and enable the APIs, return to this tutorial by clicking
+   on the **Cloud Shell** tab in your browser.
+    
+   * [Enable APIs](https://console.cloud.google.com/flows/enableapi?apiid=dataproc,compute_component,storage-component.googleapis.com&redirect=https://console.cloud.google.com)
 
 ## Prerequisites (2)
 
@@ -140,7 +145,8 @@ Job output in Cloud Shell shows cluster creation, job submission,
 ### Next Steps:
 
 * **View job details from the Console.** View job details by selecting the
-   PySpark job from the Cloud Dataproc
+   PySpark job from the Dataproc 
+=
    [Jobs page](https://console.cloud.google.com/dataproc/jobs)
    in the Google Cloud Platform Console.
 
@@ -160,5 +166,5 @@ Job output in Cloud Shell shows cluster creation, job submission,
    gsutil rm -r gs://$BUCKET
    ```
 
-* **For more information.** See the [Cloud Dataproc documentation](https://cloud.google.com/dataproc/docs/)
+* **For more information.** See the [Dataproc documentation](https://cloud.google.com/dataproc/docs/)
    for API reference and product feature information.