PMLE Scaling Prototypes into ML Models Practice Question
A data scientist needs to train a large PyTorch model on a custom dataset using Vertex AI. The training script expects data from Cloud Storage and uses GPU acceleration. Which option correctly configures a custom training job with a pre-built container for PyTorch and attaches a single NVIDIA V100 GPU?
⚠ Common exam trap
PMLE often tests the exact API field names and the distinction between Vertex AI custom training and legacy AI Platform Training — candidates pick 'custom container' or 'AI Platform Training' because they sound plausible, but only the worker_pool_specs configuration with the pre-built PyTorch GPU image and correct accelerator_type is valid on Vertex AI.
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 the pre-built container 'us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.1-12:latest' and in worker_pool_specs set machine_type='n1-standard-4', accelerator_type='NVIDIA_TESLA_V100', accelerator_count=1
Vertex AI custom training jobs use worker_pool_specs to define the machine type, container image, and accelerator configuration. The correct configuration uses the pre-built PyTorch GPU container and specifies machine_type='n1-standard-4', accelerator_type='NVIDIA_TESLA_V100', and accelerator_count=1 in the worker pool spec. This is the documented Vertex AI pattern for attaching a single V100 GPU.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a custom container built from PyTorch base image and specify accelerator_count=1 in the machine spec
Why it's wrong here
A custom container built from a PyTorch base image bypasses the pre-built PyTorch container the stem requires, and accelerator_count=1 alone does not pin the accelerator type to NVIDIA V100. It is tempting because custom containers offer full control, and would be correct when the script needs dependencies absent from pre-built images.
- ✓
Use the pre-built container 'us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.1-12:latest' and in worker_pool_specs set machine_type='n1-standard-4', accelerator_type='NVIDIA_TESLA_V100', accelerator_count=1
Why this is correct
The PyTorch GPU pre-built container supplies the training runtime, while worker_pool_specs declares machine_type, accelerator_type='NVIDIA_TESLA_V100' and accelerator_count=1, attaching exactly one V100. This satisfies both the custom PyTorch training and single-GPU constraints without building a custom image.
- ✗
Use the AI Platform Training service with gcloud ai-platform jobs submit training and --scale-tier BASIC_GPU
Why it's wrong here
AI Platform Training is the legacy service, not Vertex AI, and BASIC_GPU does not guarantee a single NVIDIA V100 accelerator. It is tempting because it submits custom training jobs with GPUs, and would be correct for older AI Platform workloads rather than the Vertex AI custom job specified.
- ✗
Create a training pipeline with AutoML and select GPU runtime
Why it's wrong here
AutoML trains Google's models on tabular, image, text or video data; it cannot run a custom PyTorch training script or attach a chosen V100 GPU. It is tempting because AutoML pipelines expose GPU runtime settings, and would be correct for no-code model training on supported data types.
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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
This PMLE 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 PMLE exam.