PDE Maintaining and Automating Data Workloads Practice Question
You manage a Cloud Composer 2 environment that runs a DAG with a task using the BigQueryInsertJobOperator. The task occasionally fails with 'rateLimitExceeded' when submitting many jobs in parallel. You want to limit the number of concurrent BigQuery jobs submitted by this DAG without affecting other DAGs in the same environment. What should you do?
⚠ Common exam trap
A common mix-up: candidates confuse task-level concurrency controls like max_active_tasks with the need to throttle a specific external service, which is best handled by Airflow pools.
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
✓
Use a pool with a limited number of slots and assign the BigQuery tasks to that pool.
Airflow pools are designed to limit parallelism for a set of tasks. Creating a pool with a limited number of slots and assigning the BigQuery tasks to it ensures that only a controlled number of BigQuery jobs are submitted concurrently, preventing rateLimitExceeded errors. This approach is scoped to the specific tasks and does not affect other DAGs or tasks in the environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a pool with a limited number of slots and assign the BigQuery tasks to that pool.
Why this is correct
Airflow pools allow you to limit parallelism for a specific set of tasks. By creating a pool with a small number of slots and assigning the BigQuery tasks to it, you control how many BigQuery jobs run concurrently, directly addressing the rate limit. This does not affect other DAGs or tasks that are not assigned to the pool, providing a targeted solution.
- ✗
Increase the number of workers in the Cloud Composer environment to distribute the load.
Why it's wrong here
Adding workers increases the capacity to run tasks, which could actually increase the rate of BigQuery job submissions and worsen the rate limit issue. It does not provide a mechanism to throttle submissions. This option moves in the opposite direction of what is needed.
- ✗
Configure the BigQueryInsertJobOperator with a lower priority for the jobs.
Why it's wrong here
Job priority affects the order in which BigQuery executes queued jobs, but it does not reduce the rate at which jobs are submitted. The rateLimitExceeded error occurs at submission time, so lowering priority will not prevent the error. This option does not address the concurrency of submissions.
- ✗
Set max_active_tasks on the DAG to a low value.
Why it's wrong here
max_active_tasks limits the number of task instances that can run concurrently for the entire DAG, but it does not specifically limit BigQuery job submissions. It could reduce parallelism but may also slow down unrelated tasks. It is not a targeted solution for BigQuery rate limits and could inadvertently affect other tasks in the DAG.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.