- A
Use a custom container built from PyTorch base image and specify accelerator_count=1 in the machine spec
Why wrong: While a custom container could work, the question specifies a pre-built container. Also, accelerator_count is deprecated in favour of accelerator_type and accelerator_count.
- B
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
This correctly uses a pre-built container, sets the proper machine type and GPU accelerator.
- C
Use the AI Platform Training service with gcloud ai-platform jobs submit training and --scale-tier BASIC_GPU
Why wrong: AI Platform Training is the older service; Vertex AI is the current unified platform. The command uses deprecated service.
- D
Create a training pipeline with AutoML and select GPU runtime
Why wrong: AutoML is for automated model training, not custom PyTorch scripts with GPU.
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 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?
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
In Vertex AI, worker_pool_specs define machine types and accelerators. Using a pre-built container for PyTorch 1.12 with image_uri 'us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.1-12:latest' and specifying worker_count=1, machine_type='n1-standard-4' (or similar), and accelerator_type='NVIDIA_TESLA_V100' with count=1 sets up the job correctly.
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.
- ✗
Use a custom container built from PyTorch base image and specify accelerator_count=1 in the machine spec
Why it's wrong here
While a custom container could work, the question specifies a pre-built container. Also, accelerator_count is deprecated in favour of accelerator_type and accelerator_count.
- ✓
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
This correctly uses a pre-built container, sets the proper machine type and GPU accelerator.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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 older service; Vertex AI is the current unified platform. The command uses deprecated service.
- ✗
Create a training pipeline with AutoML and select GPU runtime
Why it's wrong here
AutoML is for automated model training, not custom PyTorch scripts with GPU.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Command / output trap
AI Platform Training is the older service; Vertex AI is the current unified platform. The command uses deprecated service.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this PMLE question test?
Scaling Prototypes into ML Models — This question tests Scaling Prototypes into ML Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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 — In Vertex AI, worker_pool_specs define machine types and accelerators. Using a pre-built container for PyTorch 1.12 with image_uri 'us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.1-12:latest' and specifying worker_count=1, machine_type='n1-standard-4' (or similar), and accelerator_type='NVIDIA_TESLA_V100' with count=1 sets up the job correctly.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
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.
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