Explanation:
This scenario illustrates a nuanced aspect of the Databricks REST API. When creating a new job, the name parameter in the JSON payload is optional, defaulting to Untitled if not specified. This means multiple jobs can be created with the same name without triggering an error. In this case, both jobs will be successfully created, each named new_job, but they will have unique identifiers (IDs) to distinguish them within Databricks.
Exam Tip: Posting the same JSON multiple times results in exact copies of the job being created, all sharing the same name but having different IDs. The ID serves as a unique identifier for jobs in Databricks.
More Info: For further details, refer to the documentation on creating a new job through the Databricks REST API.
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A new data engineer attempts to create a job using the Databricks REST API by posting the following JSON to the 2.0/jobs/create endpoint:
{
"name": "new_job",
"existing_cluster_id": "1198-132537-dht25rtr",
"spark_python_task": {
"python_file": "dbfs:/first_method.py"
}
}
{
"name": "new_job",
"existing_cluster_id": "1198-132537-dht25rtr",
"spark_python_task": {
"python_file": "dbfs:/first_method.py"
}
}
Another data engineer from the same team tries to create a job using the following JSON:
{
"name": "new_job",
"existing_cluster_id": "1198-132537-dht25rtr",
"spark_python_task": {
"python_file": "dbfs:/example.py"
}
}
{
"name": "new_job",
"existing_cluster_id": "1198-132537-dht25rtr",
"spark_python_task": {
"python_file": "dbfs:/example.py"
}
}
Assuming the first job is created successfully, what happens when the second data engineer tries to create the job?
A
The job will be created successfully with both jobs named new_job.
B
The job will be created successfully with the second job named new_job_1.
C
The job will not be created as a job with the same name already exists.
D
The job will be created successfully by overwriting the previous job as two jobs cannot share a name.
E
The task in the second job will be appended to the existing job.
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