- A
Switch from Cloud Composer to Cloud Scheduler for simpler workloads.
Why wrong: Cloud Scheduler lacks the workflow orchestration capabilities needed for complex pipelines.
- B
Reduce the concurrency of all DAGs to fit within available workers.
Why wrong: This may impact business SLA; it is a workaround, not a scalable solution.
- C
Use the GKE-based Composer environment, which provides autoscaling of Airflow workers.
GKE-based Composer auto-scales worker pods, handling variable loads effectively.
- D
Increase the parallelism setting in the Airflow configuration.
Why wrong: Parallelism controls how many tasks run simultaneously, but without more workers, tasks will queue or fail.
Question 729 of 1,000
PDE Ensuring solution quality Practice Question
This PDE practice question tests your understanding of ensuring solution quality. 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 data engineering team uses Cloud Composer (Airflow) for workflow orchestration. They notice DAG runs frequently fail, and the error indicates insufficient Airflow workers. The team wants to ensure reliable execution. Which approach best addresses the issue?
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 the GKE-based Composer environment, which provides autoscaling of Airflow workers.
Option C is correct because Cloud Composer environments backed by GKE (Google Kubernetes Engine) can automatically scale the number of Airflow workers based on the workload. This autoscaling capability directly addresses the 'insufficient Airflow workers' error by dynamically adding worker pods when the queue of tasks grows, ensuring reliable execution without manual intervention.
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.
- ✗
Switch from Cloud Composer to Cloud Scheduler for simpler workloads.
Why it's wrong here
Cloud Scheduler lacks the workflow orchestration capabilities needed for complex pipelines.
- ✗
Reduce the concurrency of all DAGs to fit within available workers.
Why it's wrong here
This may impact business SLA; it is a workaround, not a scalable solution.
- ✓
Use the GKE-based Composer environment, which provides autoscaling of Airflow workers.
Why this is correct
GKE-based Composer auto-scales worker pods, handling variable loads effectively.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the parallelism setting in the Airflow configuration.
Why it's wrong here
Parallelism controls how many tasks run simultaneously, but without more workers, tasks will queue or fail.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between configuration parameters that control task scheduling (like `parallelism` or `concurrency`) and infrastructure-level scaling mechanisms; the trap here is assuming that increasing `parallelism` alone can resolve worker shortages, when in fact it only increases the demand on a fixed pool of workers.
Detailed technical explanation
How to think about this question
In a GKE-based Cloud Composer environment, Airflow workers run as Kubernetes pods managed by a Horizontal Pod Autoscaler (HPA). The HPA monitors CPU/memory utilization or custom metrics (e.g., queue depth) and automatically adjusts the replica count of worker pods. This is distinct from the `parallelism` setting, which is a scheduler-level limit; without autoscaling, even a high parallelism value will cause tasks to queue or fail if the fixed number of worker pods cannot execute them. In real-world scenarios, bursty workloads (e.g., end-of-month data processing) benefit greatly from autoscaling, whereas static environments require over-provisioning to avoid failures.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
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FAQ
Questions learners often ask
What does this PDE question test?
Ensuring solution quality — This question tests Ensuring solution quality — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the GKE-based Composer environment, which provides autoscaling of Airflow workers. — Option C is correct because Cloud Composer environments backed by GKE (Google Kubernetes Engine) can automatically scale the number of Airflow workers based on the workload. This autoscaling capability directly addresses the 'insufficient Airflow workers' error by dynamically adding worker pods when the queue of tasks grows, ensuring reliable execution without manual intervention.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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