diff --git a/common/src/main/java/org/opensearch/ml/common/connector/functions/postprocess/BedrockBatchJobArnPostProcessFunction.java b/common/src/main/java/org/opensearch/ml/common/connector/functions/postprocess/BedrockBatchJobArnPostProcessFunction.java
index 7ecaf15f32..88f7cad061 100644
--- a/common/src/main/java/org/opensearch/ml/common/connector/functions/postprocess/BedrockBatchJobArnPostProcessFunction.java
+++ b/common/src/main/java/org/opensearch/ml/common/connector/functions/postprocess/BedrockBatchJobArnPostProcessFunction.java
@@ -9,10 +9,13 @@
 import org.opensearch.ml.common.output.model.ModelTensor;
 
 import java.util.ArrayList;
+import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
 
-public class BedrockBatchJobArnPostProcessFunction extends ConnectorPostProcessFunction<List<Float>> {
+public class BedrockBatchJobArnPostProcessFunction extends ConnectorPostProcessFunction<Map<String, String>> {
+    public static final String JOB_ARN = "jobArn";
+    public static final String PROCESSED_JOB_ARN = "processedJobArn";
 
     @Override
     public void validate(Object input) {
@@ -22,22 +25,25 @@ public void validate(Object input) {
 
         Map<String, String> jobInfo = (Map<String, String>) input;
 
-        if (!(jobInfo.containsKey("jobArn"))) {
+        if (!(jobInfo.containsKey(JOB_ARN))) {
             throw new IllegalArgumentException("Bedrock batch job arn missing.");
         }
     }
 
     @Override
-    public List<ModelTensor> process(List<Float> embedding) {
+    public List<ModelTensor> process(Map<String, String> jobInfo) {
         List<ModelTensor> modelTensors = new ArrayList<>();
+
+        Map<String, String> processedResult = new HashMap<>();
+        processedResult.putAll(jobInfo);
+        String jobArn = jobInfo.get(JOB_ARN);
+        processedResult.put(PROCESSED_JOB_ARN, jobArn.replace("/", "%2F"));
         modelTensors
             .add(
                 ModelTensor
                     .builder()
-                    .name("sentence_embedding")
-                    .dataType(MLResultDataType.FLOAT32)
-                    .shape(new long[] { embedding.size() })
-                    .data(embedding.toArray(new Number[0]))
+                    .name("response")
+                    .dataAsMap(processedResult)
                     .build()
             );
         return modelTensors;