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PDE Practice Question: A company uses Dataproc for transient clusters
A company uses Dataproc for transient clusters. Which TWO actions can reduce costs?
⚠ Common exam trap
Candidates often think 'persistent clusters' are cheaper because they avoid re-creation overhead, but they overlook the continuous compute cost of idle persistent clusters versus the pay-per-use model of transient clusters.
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
✓
Set cluster autoscaling to minimize idle resources
Dataproc cluster autoscaling automatically adjusts the number of worker nodes based on the YARN memory and CPU utilization metrics. By scaling down during idle periods, you avoid paying for unused compute capacity, directly reducing costs for transient clusters that have variable workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase master node size
Why it's wrong here
Larger master node increases cost.
- ✓
Set cluster autoscaling to minimize idle resources
Why this is correct
Autoscaling reduces resource waste, lowering cost.
- ✗
Use standard VMs for all nodes
Why it's wrong here
Standard VMs are more expensive than preemptible.
- ✗
Use persistent clusters to avoid creation overhead
Why it's wrong here
Persistent clusters cost when idle.
- ✓
Use preemptible VMs for worker nodes
Why this is correct
Preemptible VMs are cheaper and suitable for transient workloads.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.