chore: regenerate dlp client (googleapis#25298)

Generated in GitHub action: https://togithub.com/googleapis/googleapis/google-api-java-client-services/actions/workflows/codegen.yaml

Author: yoshi-code-bot

clients/google-api-services-dlp/v2/2.0.0/README.md

@@ -22,7 +22,7 @@ Add the following lines to your `pom.xml` file:
     <dependency>
       <groupId>com.google.apis</groupId>
       <artifactId>google-api-services-dlp</artifactId>
-      <version>v2-rev20250105-2.0.0</version>
+      <version>v2-rev20250119-2.0.0</version>
     </dependency>
   </dependencies>
 </project>
@@ -35,7 +35,7 @@ repositories {
   mavenCentral()
 }
 dependencies {
-  implementation 'com.google.apis:google-api-services-dlp:v2-rev20250105-2.0.0'
+  implementation 'com.google.apis:google-api-services-dlp:v2-rev20250119-2.0.0'
 }
 ```
 

clients/google-api-services-dlp/v2/2.0.0/com/google/api/services/dlp/v2/model/GooglePrivacyDlpV2Export.java

@@ -35,19 +35,20 @@ public final class GooglePrivacyDlpV2Export extends com.google.api.client.json.G
    * Store all profiles to BigQuery. * The system will create a new dataset and table for you if
    * none are are provided. The dataset will be named `sensitive_data_protection_discovery` and
    * table will be named `discovery_profiles`. This table will be placed in the same project as the
-   * container project running the scan. The configuration will be updated with the fields set after
-   * the first profile is generated and the dataset and table are created. * See [Analyze data
-   * profiles stored in BigQuery](https://cloud.google.com/sensitive-data-protection/docs/analyze-
-   * data-profiles) * See [Sample queries for your BigQuery
+   * container project running the scan. After the first profile is generated and the dataset and
+   * table are created, the discovery scan configuration will be updated with the dataset and table
+   * names. * See [Analyze data profiles stored in BigQuery](https://cloud.google.com/sensitive-
+   * data-protection/docs/analyze-data-profiles). * See [Sample queries for your BigQuery
    * table](https://cloud.google.com/sensitive-data-protection/docs/analyze-data-
    * profiles#sample_sql_queries). * Data is inserted using [streaming
    * insert](https://cloud.google.com/blog/products/bigquery/life-of-a-bigquery-streaming-insert)
    * and so data may be in the buffer for a period of time after the profile has finished. * The
    * Pub/Sub notification is sent before the streaming buffer is guaranteed to be written, so data
    * may not be instantly visible to queries by the time your topic receives the Pub/Sub
    * notification. * The best practice is to use the same table for an entire organization so that
-   * you can take advantage of the provided Looker reports. If you use VPC Service Controls to
-   * define security perimeters, then you must use a separate table for each boundary.
+   * you can take advantage of the [provided Looker reports](https://cloud.google.com/sensitive-
+   * data-protection/docs/analyze-data-profiles#use_a_premade_report). If you use VPC Service
+   * Controls to define security perimeters, then you must use a separate table for each boundary.
    * The value may be {@code null}.
    */
   @com.google.api.client.util.Key
@@ -57,19 +58,20 @@ public final class GooglePrivacyDlpV2Export extends com.google.api.client.json.G
    * Store all profiles to BigQuery. * The system will create a new dataset and table for you if
    * none are are provided. The dataset will be named `sensitive_data_protection_discovery` and
    * table will be named `discovery_profiles`. This table will be placed in the same project as the
-   * container project running the scan. The configuration will be updated with the fields set after
-   * the first profile is generated and the dataset and table are created. * See [Analyze data
-   * profiles stored in BigQuery](https://cloud.google.com/sensitive-data-protection/docs/analyze-
-   * data-profiles) * See [Sample queries for your BigQuery
+   * container project running the scan. After the first profile is generated and the dataset and
+   * table are created, the discovery scan configuration will be updated with the dataset and table
+   * names. * See [Analyze data profiles stored in BigQuery](https://cloud.google.com/sensitive-
+   * data-protection/docs/analyze-data-profiles). * See [Sample queries for your BigQuery
    * table](https://cloud.google.com/sensitive-data-protection/docs/analyze-data-
    * profiles#sample_sql_queries). * Data is inserted using [streaming
    * insert](https://cloud.google.com/blog/products/bigquery/life-of-a-bigquery-streaming-insert)
    * and so data may be in the buffer for a period of time after the profile has finished. * The
    * Pub/Sub notification is sent before the streaming buffer is guaranteed to be written, so data
    * may not be instantly visible to queries by the time your topic receives the Pub/Sub
    * notification. * The best practice is to use the same table for an entire organization so that
-   * you can take advantage of the provided Looker reports. If you use VPC Service Controls to
-   * define security perimeters, then you must use a separate table for each boundary.
+   * you can take advantage of the [provided Looker reports](https://cloud.google.com/sensitive-
+   * data-protection/docs/analyze-data-profiles#use_a_premade_report). If you use VPC Service
+   * Controls to define security perimeters, then you must use a separate table for each boundary.
    * @return value or {@code null} for none
    */
   public GooglePrivacyDlpV2BigQueryTable getProfileTable() {
@@ -80,19 +82,20 @@ public GooglePrivacyDlpV2BigQueryTable getProfileTable() {
    * Store all profiles to BigQuery. * The system will create a new dataset and table for you if
    * none are are provided. The dataset will be named `sensitive_data_protection_discovery` and
    * table will be named `discovery_profiles`. This table will be placed in the same project as the
-   * container project running the scan. The configuration will be updated with the fields set after
-   * the first profile is generated and the dataset and table are created. * See [Analyze data
-   * profiles stored in BigQuery](https://cloud.google.com/sensitive-data-protection/docs/analyze-
-   * data-profiles) * See [Sample queries for your BigQuery
+   * container project running the scan. After the first profile is generated and the dataset and
+   * table are created, the discovery scan configuration will be updated with the dataset and table
+   * names. * See [Analyze data profiles stored in BigQuery](https://cloud.google.com/sensitive-
+   * data-protection/docs/analyze-data-profiles). * See [Sample queries for your BigQuery
    * table](https://cloud.google.com/sensitive-data-protection/docs/analyze-data-
    * profiles#sample_sql_queries). * Data is inserted using [streaming
    * insert](https://cloud.google.com/blog/products/bigquery/life-of-a-bigquery-streaming-insert)
    * and so data may be in the buffer for a period of time after the profile has finished. * The
    * Pub/Sub notification is sent before the streaming buffer is guaranteed to be written, so data
    * may not be instantly visible to queries by the time your topic receives the Pub/Sub
    * notification. * The best practice is to use the same table for an entire organization so that
-   * you can take advantage of the provided Looker reports. If you use VPC Service Controls to
-   * define security perimeters, then you must use a separate table for each boundary.
+   * you can take advantage of the [provided Looker reports](https://cloud.google.com/sensitive-
+   * data-protection/docs/analyze-data-profiles#use_a_premade_report). If you use VPC Service
+   * Controls to define security perimeters, then you must use a separate table for each boundary.
    * @param profileTable profileTable or {@code null} for none
    */
   public GooglePrivacyDlpV2Export setProfileTable(GooglePrivacyDlpV2BigQueryTable profileTable) {

clients/google-api-services-dlp/v2/2.0.0/com/google/api/services/dlp/v2/model/GooglePrivacyDlpV2InfoTypeDescription.java

@@ -58,7 +58,7 @@ public final class GooglePrivacyDlpV2InfoTypeDescription extends com.google.api.
   private java.lang.String displayName;
 
   /**
-   * A sample true positive for this infoType.
+   * A sample that is a true positive for this infoType.
    * The value may be {@code null}.
    */
   @com.google.api.client.util.Key
@@ -144,15 +144,15 @@ public GooglePrivacyDlpV2InfoTypeDescription setDisplayName(java.lang.String dis
   }
 
   /**
-   * A sample true positive for this infoType.
+   * A sample that is a true positive for this infoType.
    * @return value or {@code null} for none
    */
   public java.lang.String getExample() {
     return example;
   }
 
   /**
-   * A sample true positive for this infoType.
+   * A sample that is a true positive for this infoType.
    * @param example example or {@code null} for none
    */
   public GooglePrivacyDlpV2InfoTypeDescription setExample(java.lang.String example) {

clients/google-api-services-dlp/v2/2.0.0/pom.xml

@@ -8,8 +8,8 @@
 
   <groupId>com.google.apis</groupId>
   <artifactId>google-api-services-dlp</artifactId>
-  <version>v2-rev20250105-2.0.0</version>
-  <name>Sensitive Data Protection (DLP) v2-rev20250105-2.0.0</name>
+  <version>v2-rev20250119-2.0.0</version>
+  <name>Sensitive Data Protection (DLP) v2-rev20250119-2.0.0</name>
   <packaging>jar</packaging>
 
   <inceptionYear>2011</inceptionYear>

clients/google-api-services-dlp/v2/README.md

@@ -22,7 +22,7 @@ Add the following lines to your `pom.xml` file:
     <dependency>
       <groupId>com.google.apis</groupId>
       <artifactId>google-api-services-dlp</artifactId>
-      <version>v2-rev20250105-2.0.0</version>
+      <version>v2-rev20250119-2.0.0</version>
     </dependency>
   </dependencies>
 </project>
@@ -35,7 +35,7 @@ repositories {
   mavenCentral()
 }
 dependencies {
-  implementation 'com.google.apis:google-api-services-dlp:v2-rev20250105-2.0.0'
+  implementation 'com.google.apis:google-api-services-dlp:v2-rev20250119-2.0.0'
 }
 ```