Cloud Digital Leader Why Cloud Technology Can Transform Business Practice Question
A company runs a batch job that processes data every night. The job can tolerate interruptions and currently runs on a dedicated on-premises server that is underutilized. They want to migrate to Google Cloud and minimise compute cost. Which compute option is most cost-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
✓
Preemptible VMs
Preemptible VMs offer significantly lower cost (up to 80% discount) but can be terminated at any time, making them ideal for fault-tolerant batch workloads. Standard VMs are more expensive, sole-tenant nodes are for isolation, and GPUs add cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Sole-tenant nodes
Why it's wrong here
Sole-tenant nodes dedicate physical servers exclusively to a single customer, providing hardware isolation for compliance or licensing requirements. They carry a substantial premium over standard Compute Engine pricing because the entire host is reserved regardless of actual utilization. For a nightly batch job that needs no special isolation, sole-tenant nodes would drastically increase cost without adding any operational benefit.
- ✗
Standard (on-demand) VMs
Why it's wrong here
Standard (on-demand) VMs are billed at the full list price with no committed-use discount or preemptible savings, guaranteeing continuous operation until you delete them. That guarantee is unnecessary for a fault-tolerant nightly batch workload, which can be safely restarted if the underlying instance is reclaimed. Paying on-demand rates for a job that can tolerate interruption means leaving significant cost savings on the table for no added reliability.
- ✗
VMs with GPUs
Why it's wrong here
VMs with GPUs attach expensive accelerators designed for vector- and matrix-heavy computations like machine learning, graphics rendering, or scientific simulation. A generic data-processing batch job rarely performs such specialized work, so the GPU would sit idle while the CPU handles the workload. Adding GPU instances for a CPU-bound nightly job increases cost per hour substantially and is the opposite of the cost-optimization goal this scenario targets.
- ✓
Preemptible VMs
Why this is correct
Preemptible VMs are Compute Engine instances that use leftover capacity at a discount of as much as 60-80% compared to on-demand pricing, but they can be terminated by Google at any time with a 30-second warning. Because a nightly batch job is inherently fault-tolerant and can be designed to resume from a checkpoint, it is an ideal fit for these interruptible instances. The key is to architect the job with idempotent processing and persistent storage so that preemption does not corrupt data or lose progress, making it the correct, cost-effective choice.
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Building a Data-Driven Culture
Key term
Google Cloud
Google Cloud is a suite of cloud computing services offered by Google that provides infrastructure, platform, and software solutions over the internet.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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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.