Google PCA Practice Question: Managing and Provisioning a Solution Infrastructure
A startup runs a batch analytics job on a single Compute Engine instance that takes about nine hours and reads 2 TB from a Cloud Storage bucket each run. The team wants to reduce cost without changing the application code, and the job can be interrupted and resumed from checkpoints. Which machine configuration should the architect recommend?
⚠ Common exam trap
The trap here is assuming preemptible capacity is unsafe for long jobs, when checkpointing and external data storage make interruption harmless.
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
✓
A Spot VM with local SSD scratch space, since the job reads data from Cloud Storage and can resume from checkpoints.
Spot VMs provide the largest discount available on Compute Engine and are appropriate when a workload can tolerate preemption and resume from checkpoints. Because the job reads its source data from Cloud Storage and only uses local SSD as scratch, losing the instance mid-run does not corrupt results, so the cost reduction comes with acceptable risk.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A Spot VM with local SSD scratch space, since the job reads data from Cloud Storage and can resume from checkpoints.
Why this is correct
Spot VMs offer deep discounts over on-demand pricing and can be preempted at any time, which is acceptable because the job checkpoints and resumes. Reading source data from Cloud Storage means local SSD is only scratch space, so losing it on preemption does not threaten the workload, making this the most cost-effective fit.
- ✗
A sole-tenant node with a custom machine type sized to the job's peak memory usage.
Why it's wrong here
Sole-tenant nodes carry a premium for physical isolation that a batch analytics job does not require, and custom sizing does not address the interruption tolerance the job already has. This raises cost rather than lowering it, contrary to the stated goal.
- ✗
A standard predefined machine type with a balanced persistent disk, billed on demand.
Why it's wrong here
On-demand billing for a nine-hour job with no interruption tolerance requirement ignores the checkpointing capability, which is precisely what makes preemptible capacity viable. A balanced persistent disk also adds cost that is unnecessary for a read-heavy batch workload, so this choice leaves savings on the table.
- ✗
A committed use discount for a one-year term on a memory-optimized machine type.
Why it's wrong here
A one-year commitment is wasteful for a single batch job and locks in spend regardless of actual usage, while a memory-optimized type is unnecessary for a read-heavy analytics workload. Committed use discounts suit steady-state, always-on workloads, not an interruptible nine-hour job.
Go deeper
Related to this question
Learn chapter
Cloud SQL and Managed Data Stores
Key term
Compute Engine
Compute Engine is Google Cloud's Infrastructure-as-a-Service (IaaS) offering that lets you create and run virtual machines on Google's infrastructure.
Key term
CAN
A CAN (Controller Area Network) is a robust vehicle bus standard designed to allow microcontrollers and devices to communicate with each other without a host computer.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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