easyMultiple Choice
PMLE Practice Question: A company uses Cloud Composer to orchestrate…
A company uses Cloud Composer to orchestrate their ML pipelines. They notice that tasks are being queued but not executed, causing delays. What is the most likely cause?
⚠ Common exam trap
Candidates often confuse the roles of Airflow components (web server, scheduler, worker) and assume a UI or DAG access issue causes queued tasks, when in reality the worker capacity is the bottleneck.
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 Airflow worker resources are exhausted
When tasks are queued but not executed, it typically indicates that the Airflow workers have no available slots to pick up new tasks. In Cloud Composer, the Celery executor distributes tasks to workers; if all worker concurrency slots are saturated or the worker node pool is under-provisioned, tasks remain in the 'queued' state until a worker becomes free. This is the most likely cause given the symptom of tasks being queued without execution.
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 Airflow web server is down
Why it's wrong here
The web server only serves the Airflow UI and API; it plays no part in scheduling or executing queued tasks, so its outage cannot cause this symptom. It is tempting because the UI becomes unreachable, which looks like pipeline failure, but the correct cause is exhausted worker capacity or a saturated executor queue.
- ✗
The DAG file is corrupted
Why it's wrong here
A corrupted DAG file prevents the scheduler from parsing it, so its tasks never enter the queue at all; queued-but-not-executed tasks indicate insufficient worker slots or capacity. Corruption is tempting because malformed DAGs do cause failures, but that manifests as import errors and missing tasks, not queueing.
- ✗
The Cloud Storage bucket containing DAGs is not accessible
Why it's wrong here
An inaccessible DAG bucket stops the scheduler syncing DAGs, so tasks never appear or update rather than queueing. It is tempting because Cloud Composer stores DAGs in Cloud Storage, and permission errors do break pipelines, but queued tasks already parsed successfully, pointing instead to insufficient worker capacity.
- ✓
The Airflow worker resources are exhausted
Why this is correct
Queued-but-unexecuted tasks mean the scheduler has work but no slot to run it. Exhausted worker CPU or memory on the Composer cluster prevents Airflow from starting queued task instances, so the queue grows while nothing executes. Scaling worker count or resources resolves the backlog.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE 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 PMLE exam.