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PDE Practice Question: A company uses Cloud Composer (Airflow) to…
A company uses Cloud Composer (Airflow) to orchestrate a data pipeline. One DAG has many tasks that run in parallel and dependencies that span multiple days. Recently, the DAG started failing with 'DagRun already exists' errors. What is the most likely cause?
⚠ Common exam trap
Google Cloud often tests the distinction between DAG-level concurrency settings (max_active_runs_per_dag) and task-level parallelism (e.g., pool, task concurrency), leading candidates to confuse the 'DagRun already exists' error with scheduler overload or task dependency issues.
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
✓
The DAG has max_active_runs_per_dag set to a low number, causing overlapping runs to be rejected.
The 'DagRun already exists' error occurs when Airflow attempts to create a new DAG run for a logical date that already has an active or completed run, and the DAG's concurrency settings prevent overlapping runs. Setting max_active_runs_per_dag to a low number (e.g., 1) restricts the number of concurrent runs, so if a previous run hasn't finished or been cleared, a new run for the same or overlapping schedule interval is rejected with this error. This is the most likely cause given the DAG has dependencies spanning multiple days, which can cause runs to overlap if not properly configured.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The DAG has a large number of tasks, overwhelming the Airflow scheduler.
Why it's wrong here
Scheduler overload leads to queuing, not a DAG run duplicate error.
- ✓
The DAG has max_active_runs_per_dag set to a low number, causing overlapping runs to be rejected.
Why this is correct
If max_active_runs_per_dag is too low, a new DAG run cannot start while the previous one is active.
- ✗
The DAG's schedule interval is too short, causing task instances to be created with duplicate run IDs.
Why it's wrong here
Duplicate run IDs are not the error; DAG run IDs are unique per execution date.
- ✗
The DAG has a depends_on_past set to True, causing upstream failures to block new runs.
Why it's wrong here
depends_on_past would cause task failures, not a DAG run error.
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