Cloud Digital Leader Preemptible VMs Practice Question
A company runs batch processing jobs using preemptible VMs to reduce costs. They need to ensure these jobs can scale out significantly during peak hours. Which Compute Engine pricing model should they combine with autoscaling to optimize cost for these workloads?
⚠ Common exam trap
Candidates may think they can stack additional discounts on preemptible VMs, but sustained use discounts do not apply. The trap is choosing a discount model that sounds attractive but is not compatible with the chosen VM type.
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
✓
Preemptible VMs with no further discounts.
Preemptible VMs already provide significant cost savings (up to 60-91% off standard VMs) and are ideal for fault-tolerant batch jobs. Combining them with autoscaling allows scaling during peak hours without any additional discounts. Sustained use discounts do not apply to preemptible VMs, and committed use discounts require upfront commitments unsuitable for variable workloads. Sole-tenant nodes are designed for dedicated hardware, not cost optimization. Therefore, using preemptible VMs with no further discounts is the optimal cost-effective solution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Preemptible VMs with no further discounts.
Why this is correct
Preemptible VMs offer the lowest compute price but cannot receive sustained use or committed use discounts, so no further discount applies. Combined with autoscaling, they scale out cheaply during peak hours while tolerating preemption, satisfying the batch cost constraint.
- ✗
Sole-tenant nodes.
Why it's wrong here
Sole-tenant nodes provide physical isolation on dedicated hardware, billed per node regardless of usage. They are tempting for compliance or licensing, but they do not offer the discounted, interruptible capacity that preemptible workloads need to scale out cheaply during peaks.
- ✗
Sustained use discounts.
Why it's wrong here
Sustained use discounts apply automatically to continuous usage and cannot be combined with autoscaling preemptible capacity for bursty batch work. They are tempting as a cost lever, but the correct model is a committed use or spot/preemptible combination, since SUDs reward steady baseline consumption rather than peak scaling.
- ✗
Committed use discounts.
Why it's wrong here
Committed use discounts require a one- or three-year spend commitment for steady-state workloads. They are tempting for predictable baseline usage, but preemptible batch jobs scaling elastically during peaks are not steady, so a commitment would waste money on unused capacity.
Go deeper
Related to this question
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Cloud Maturity Model and Readiness Assessment
Key term
Batch
Batch is a cloud computing service that runs large numbers of computing jobs as a group, or batch, without needing to manage individual servers.
Key term
Batch processing
Batch processing is a method of running high-volume, repetitive data jobs where a group of transactions is collected, processed together automatically, and then results are produced without real-time user interaction.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This GCDL 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 GCDL exam.