PDE Maintaining and Automating Data Workloads Practice Question
Your team uses Cloud Composer to run Apache Airflow DAGs. One DAG uses a BigQueryInsertJobOperator to run a query and then uses BigQueryCheckOperator to verify the results. The DAG is failing intermittently because the query result is not ready when the check operator runs. How should you modify the DAG to ensure the check operator runs only after the query completes successfully?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add a BigQueryTableExistenceSensor before the BigQueryCheckOperator to wait for the table to be created.
The BigQueryInsertJobOperator is synchronous by default, so the DAG should already wait for completion. However, if the check operator fails due to data not being written, adding a sensor operator (e.g., BigQueryTableExistenceSensor) between the two tasks ensures the table exists before checking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add a BigQueryTableExistenceSensor before the BigQueryCheckOperator to wait for the table to be created.
Why this is correct
Sensors wait for a condition; this ensures the table exists before the check runs.
- ✗
Change the BigQueryInsertJobOperator to use deferrable mode to make it async.
Why it's wrong here
Deferrable mode is not needed; the operator is already synchronous.
- ✗
Use a PythonOperator to call the BigQuery API directly and wait for the job to finish.
Why it's wrong here
Not necessary; the existing operator is synchronous. The issue is about data readiness.
- ✗
Increase the timeout of the BigQueryCheckOperator.
Why it's wrong here
Timeout does not address the root cause; check might still run before data is ready.
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