PMLE Scaling Prototypes into ML Models Practice Question

A data scientist wants to train a PyTorch model on Vertex AI using a pre-built container for GPU training. She needs to use 4 NVIDIA A100 GPUs on a single machine. Which machine configuration should she select?

⚠ Common exam trap

PMLE often tests machine-type specificity — candidates pick a generic n1 machine with the right GPU count, missing that A100 GPUs require the A2 machine family, not N1.

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-highgpu-4g (4 A100 GPUs)

The a2-highgpu-4g machine type is purpose-built for GPU workloads and provides exactly 4 NVIDIA A100 GPUs on a single machine, matching the requirement precisely. Vertex AI supports this machine type for custom training with pre-built containers, so the data scientist can select it directly in the training configuration. Choosing it avoids over-provisioning or mismatching GPU counts.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
n1-highmem-16 with 4 NVIDIA V100 GPUs
Why it's wrong here
V100 GPUs are not A100s, so this machine cannot meet the stated A100 requirement. V100 accelerators remain valid for older GPU training workloads, but the scenario specifies A100 hardware, making the GPU generation the disqualifying factor.
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n1-standard-16 with 4 NVIDIA T4 GPUs
Why it's wrong here
T4 GPUs are not A100s, so this configuration cannot satisfy the stated A100 requirement regardless of count. T4 accelerators suit cost-sensitive inference and lighter training; a single-machine A100 configuration is needed here, making the GPU model the deciding factor.
✓
a2-highgpu-4g (4 A100 GPUs)
Why this is correct
The a2-highgpu-4g machine type provides exactly four NVIDIA A100 GPUs on a single node, matching the stem's requirement for four A100s on one machine. This satisfies the GPU count and single-machine constraint, letting the pre-built PyTorch container train without custom configuration or distributed multi-node setup.
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a2-megagpu-16g (16 A100 GPUs)
Why it's wrong here
a2-megagpu-16g provides 16 A100 GPUs, exceeding the four required on one machine, so it over-provisions and raises cost unnecessarily. The a2 family is correct for A100 training; the right choice is the variant offering exactly four A100s.

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PMLE Scaling Prototypes into ML Models Practice Question

Why each option matters

Option-by-option breakdown

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