Google PCA Manage implementation of cloud architecture Practice Question
A company runs a batch processing workload on Compute Engine instances in a managed instance group (MIG). The job is CPU-intensive and takes approximately 4 hours to complete. The company wants to reduce costs without sacrificing performance. Which action should they take?
⚠ Common exam trap
Google Cloud often tests the misconception that committed use discounts are the best cost-saving option for any workload, but they are only cost-effective for predictable, always-on instances, not for batch jobs that can leverage preemptible VMs.
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 the MIG and implement a checkpointing mechanism to handle interruptions.
Preemptible VMs are significantly cheaper than standard VMs but can be terminated at any time. For a batch processing workload that is CPU-intensive and runs for 4 hours, using preemptible VMs in a MIG with a checkpointing mechanism allows the job to resume from the last saved state after an interruption, thus reducing costs without sacrificing performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Purchase committed use discounts for the instance type.
Why it's wrong here
Committed use discounts require a one- or three-year spend commitment, but this batch job runs only about four hours, so the commitment cannot be recovered within the workload's duration. They suit steady, predictable long-running usage, not short CPU-intensive batch runs that finish quickly.
- ✗
Change the machine series to a smaller machine type.
Why it's wrong here
A smaller machine type reduces vCPU and memory, extending the four-hour runtime and degrading performance, which the scenario forbids. Smaller types suit lightly loaded or latency-tolerant workloads, not CPU-intensive batch jobs where throughput depends on available compute.
- ✓
Use preemptible VMs for the MIG and implement a checkpointing mechanism to handle interruptions.
Why this is correct
Preemptible VMs cost substantially less than standard instances but can be reclaimed at any time, so checkpointing preserves progress across interruptions. This suits the four-hour CPU-intensive batch job, cutting cost while the MIG restarts reclaimed instances to maintain throughput.
- ✗
Provision additional reserved VMs to ensure capacity.
Why it's wrong here
Reserved VMs add fixed capacity charges regardless of use, increasing cost for a four-hour job rather than reducing it. They suit steady-state workloads needing guaranteed long-term capacity, not transient batch processing where instances should terminate on completion.
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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
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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