PDE Maintaining and Automating Data Workloads Practice Question
A company runs a Dataproc cluster for ETL jobs that process data nightly. They want to reduce costs while maintaining performance. Which strategy is MOST effective?
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 preemptible VMs for worker nodes only
Preemptible VMs are cheaper and suitable for fault-tolerant batch jobs. They can be used for worker nodes in Dataproc.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use committed use discounts for all VMs
Why it's wrong here
Committed use discounts require 1-year commitment; preemptible VMs are cheaper without commitment.
- ✗
Enable Dataproc auto-scaling
Why it's wrong here
Auto-scaling helps but does not reduce cost as much as using preemptible VMs.
- ✗
Use preemptible VMs for all nodes including master
Why it's wrong here
Master node should not be preemptible to avoid cluster instability.
- ✓
Use preemptible VMs for worker nodes only
Why this is correct
Workers can be preemptible because batch jobs can tolerate interruptions; master remains on-demand for reliability.
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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.