- A
N2-standard machine with a custom GPU attached as a standard on-demand VM
Why wrong: N2-standard machines don't support GPU attachments natively — GPU workloads use accelerator-optimized machine families (A2, A3, G2).
- B
A2 or A3 (accelerator-optimized) Spot VM with NVIDIA GPU
A2/A3 machines include NVIDIA A100/H100 GPUs designed for ML training. Using Spot VM pricing for fault-tolerant 6-hour jobs reduces cost by up to 91%.
- C
E2-highcpu Spot VM — more vCPUs provide equivalent GPU-like parallelism
Why wrong: CPU parallelism is not equivalent to GPU compute for ML training workloads — actual GPU hardware is required for tensor operations.
- D
C3 (compute-optimized) on-demand VM — best for numerically intensive workloads
Why wrong: C3 machines are optimized for high single-threaded CPU performance — they don't include GPUs and are not suited for GPU-bound ML training.
Quick Answer
The correct choice is the A2 or A3 accelerator-optimized Spot VM with an NVIDIA GPU. This combination directly addresses the need for GPU-bound machine learning training at the lowest cost because A2 and A3 families are purpose-built for high-performance GPU workloads, and Spot pricing offers a 60-91% discount over on-demand instances. Since the workload runs for only six hours and tolerates interruption, Spot VMs are ideal—they can be preempted but provide maximum GPU access for the price, making them the most cost-effective option for intermittent training jobs. On the Google Associate Cloud Engineer exam, this scenario tests your understanding of choosing the right machine family and pricing model for transient, GPU-intensive tasks; a common trap is selecting general-purpose or compute-optimized VMs, which lack dedicated GPU support. Remember the mnemonic “A2/A3 Spot = GPU Bargain” to recall that accelerator-optimized families with preemptible pricing give you the most GPU power for the least cost.
Google ACE Planning and configuring a cloud solution Practice Question
This ACE practice question tests your understanding of planning and configuring a cloud solution. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A team is selecting the right Compute Engine machine family for a machine learning training workload that is GPU-bound. The workload runs for 6 hours at a time and tolerates interruption. Which combination maximizes GPU access at lowest cost?
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
A2 or A3 (accelerator-optimized) Spot VM with NVIDIA GPU
Option B is correct because A2 and A3 Spot VMs are purpose-built for GPU-bound ML training workloads, offering direct access to NVIDIA GPUs (e.g., A100, H100) at the lowest cost due to Spot pricing (60-91% discount). The workload's 6-hour duration and interruption tolerance make Spot VMs ideal, as they can be preempted but provide maximum GPU utilization for the price.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
N2-standard machine with a custom GPU attached as a standard on-demand VM
Why it's wrong here
N2-standard machines don't support GPU attachments natively — GPU workloads use accelerator-optimized machine families (A2, A3, G2).
- ✓
A2 or A3 (accelerator-optimized) Spot VM with NVIDIA GPU
Why this is correct
A2/A3 machines include NVIDIA A100/H100 GPUs designed for ML training. Using Spot VM pricing for fault-tolerant 6-hour jobs reduces cost by up to 91%.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
E2-highcpu Spot VM — more vCPUs provide equivalent GPU-like parallelism
Why it's wrong here
CPU parallelism is not equivalent to GPU compute for ML training workloads — actual GPU hardware is required for tensor operations.
- ✗
C3 (compute-optimized) on-demand VM — best for numerically intensive workloads
Why it's wrong here
C3 machines are optimized for high single-threaded CPU performance — they don't include GPUs and are not suited for GPU-bound ML training.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that any VM family can be made GPU-capable by attaching a GPU, ignoring that only specific families (A2, A3, G2) support GPU attachment and that Spot VMs are the only cost-effective choice for interruptible workloads.
Detailed technical explanation
How to think about this question
A2 and A3 VMs use NVIDIA GPUs with dedicated high-speed interconnects (e.g., NVSwitch for A3) that enable multi-GPU scaling for distributed training, unlike general-purpose families where GPU attachment is limited by PCIe lanes. Spot VMs leverage spare capacity in Google Cloud data centers, with prices dynamically adjusted based on supply and demand, but they can be reclaimed with a 30-second notice; for a 6-hour training job, checkpointing is essential to resume from interruptions. The ACE exam expects candidates to recognize that GPU-bound workloads require accelerator-optimized families, and cost optimization mandates Spot/preemptible VMs when interruption is tolerable.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this ACE question test?
Planning and configuring a cloud solution — This question tests Planning and configuring a cloud solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: A2 or A3 (accelerator-optimized) Spot VM with NVIDIA GPU — Option B is correct because A2 and A3 Spot VMs are purpose-built for GPU-bound ML training workloads, offering direct access to NVIDIA GPUs (e.g., A100, H100) at the lowest cost due to Spot pricing (60-91% discount). The workload's 6-hour duration and interruption tolerance make Spot VMs ideal, as they can be preempted but provide maximum GPU utilization for the price.
What should I do if I get this ACE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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