The answer is that Airflow worker resources are exhausted. When tasks are queued but not executed in Cloud Composer, it directly indicates that the Celery executor’s worker concurrency slots are fully saturated or the underlying node pool is under-provisioned, leaving no available capacity to pick up new work. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of Airflow’s task lifecycle and resource management within a managed orchestration service—a common trap is to misdiagnose the issue as a DAG parsing error or a database bottleneck, but the key clue is the “queued” state without execution. Remember the memory tip: “Queued but not moving? Check the workers’ proving.”
PMLE Automating and orchestrating ML pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 is for UI; the scheduler and workers handle execution.
✗
The DAG file is corrupted
Why it's wrong here
A corrupted DAG would typically cause a parse error and the DAG would not appear, not queue tasks.
✗
The Cloud Storage bucket containing DAGs is not accessible
Why it's wrong here
Inaccessible DAGs would prevent the DAG from being loaded, not cause queued tasks.
✓
The Airflow worker resources are exhausted
Why this is correct
If workers are busy or the cluster is under-provisioned, tasks will be queued.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Cloud Composer uses the Celery executor by default, which relies on a RabbitMQ or Redis message broker to queue tasks and a set of worker pods to execute them. The `celery.worker_concurrency` Airflow configuration parameter controls how many tasks each worker can run simultaneously; when all workers reach this limit, new tasks remain in the 'queued' state in the Celery backend. In practice, this often occurs during peak load or when the environment's node count is insufficient, and can be diagnosed by checking the Airflow UI's 'Task Instances' view or the Celery worker logs for 'No more tasks available' messages.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Automating and orchestrating ML pipelines — This question tests Automating and orchestrating ML pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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