Google PCA Practice Question: Analyze and optimize technical and business processes
A company uses Cloud Composer to manage Apache Airflow workflows. They want to optimize costs. Which practice is most effective?
⚠ Common exam trap
A common mix-up: candidates assume preemptible VMs are always the best cost-saving measure, but they fail to recognize that Airflow schedulers and other critical components require persistent, reliable compute resources, making autoscaling a safer and more effective optimization.
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
✓
Configure auto scaling for the Cloud Composer environment
Cloud Composer supports autoscaling for its workers, which dynamically adjusts the number of worker pods based on the Airflow task queue depth. This directly optimizes costs by scaling down during low-load periods and scaling up only when needed, avoiding over-provisioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure auto scaling for the Cloud Composer environment
Why this is correct
Auto scaling adjusts the number of worker nodes to match Airflow workload demand, so idle capacity is not billed during quiet periods. This directly targets the cost optimisation goal without reducing the environment's ability to run scheduled workflows.
- ✗
Use preemptible VMs for Airflow schedulers
Why it's wrong here
Preemptible VMs can be reclaimed at any time, so a scheduler running on them may terminate mid-DAG, breaking orchestration. It is tempting because preemptible capacity cuts compute cost sharply, but it suits fault-tolerant batch workers, not the scheduler that must run continuously.
- ✗
Replace Cloud Composer with Cloud Functions for all workflows
Why it's wrong here
Cloud Functions cannot run Airflow's DAG scheduler, directed acyclic graph dependencies, retries, or backfills; it executes single-event functions only. It is tempting as a serverless, pay-per-invocation option, but it would be correct only for lightweight event-driven tasks, not orchestrating multi-step workflows.
- ✗
Use small machine types for all Composer components
Why it's wrong here
Shrinking machine types across every Composer component risks scheduler and worker resource exhaustion, causing task failures rather than cost savings. It is tempting because rightsizing is a genuine optimisation lever, but it would be correct only after measuring actual utilisation and confirming the workload fits the smaller shape.
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