- A
Use Vertex AI's AutoML to automatically distribute the model.
Why wrong: AutoML is for automated model building, not custom fine-tuning of large models.
- B
Use pipeline parallelism via a custom container with DeepSpeed and data parallelism across workers using PyTorch DDP, configured with Vertex AI distributed training.
This combines model and data parallelism, suitable for large models.
- C
Use Vertex AI's hyperparameter tuning with multiple trials.
Why wrong: Hyperparameter tuning is for searching hyperparameters, not for distributing model training.
- D
Configure a multi-worker mirrored strategy with TensorFlow, setting TF_CONFIG to use all GPUs on each worker.
Why wrong: Mirrored strategy replicates the entire model on each GPU; model must fit on one GPU.
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 is fine-tuning a large language model from Hugging Face using Vertex AI Training with a GPU. The model has 7 billion parameters and does not fit on a single GPU. They need to split the model across multiple GPUs and train with data parallelism. Which strategy should they use?
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 pipeline parallelism via a custom container with DeepSpeed and data parallelism across workers using PyTorch DDP, configured with Vertex AI distributed training.
Option B is correct because it combines pipeline parallelism (via DeepSpeed) to split the 7B-parameter model across multiple GPUs, with data parallelism (via PyTorch DDP) to replicate the model across workers for training on larger batches. Vertex AI distributed training coordinates the multi-worker setup, making this the only viable strategy for a model that exceeds single-GPU memory while requiring data parallelism.
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 Vertex AI's AutoML to automatically distribute the model.
Why it's wrong here
AutoML is for automated model building, not custom fine-tuning of large models.
- ✓
Use pipeline parallelism via a custom container with DeepSpeed and data parallelism across workers using PyTorch DDP, configured with Vertex AI distributed training.
Why this is correct
This combines model and data parallelism, suitable for large models.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Vertex AI's hyperparameter tuning with multiple trials.
Why it's wrong here
Hyperparameter tuning is for searching hyperparameters, not for distributing model training.
- ✗
Configure a multi-worker mirrored strategy with TensorFlow, setting TF_CONFIG to use all GPUs on each worker.
Why it's wrong here
Mirrored strategy replicates the entire model on each GPU; model must fit on one GPU.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'data parallelism' (which replicates the model) with 'model parallelism' (which splits the model), and assume a single strategy like DDP or mirrored strategy suffices, ignoring that the model must first be partitioned across GPUs using pipeline or tensor parallelism before data parallelism can be applied.
Detailed technical explanation
How to think about this question
DeepSpeed's ZeRO (Zero Redundancy Optimizer) stages, particularly ZeRO-3, partition optimizer states, gradients, and parameters across GPUs, enabling training of models like 7B parameters on multiple GPUs without fitting entirely on one. PyTorch DDP handles gradient synchronization across workers using NCCL (NVIDIA Collective Communications Library) for efficient all-reduce operations. In practice, combining DeepSpeed with Vertex AI's custom container support allows fine-tuning of models like LLaMA-2-7B on a multi-node GPU cluster, where pipeline parallelism splits transformer layers across devices and data parallelism scales batch size.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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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 pipeline parallelism via a custom container with DeepSpeed and data parallelism across workers using PyTorch DDP, configured with Vertex AI distributed training. — Option B is correct because it combines pipeline parallelism (via DeepSpeed) to split the 7B-parameter model across multiple GPUs, with data parallelism (via PyTorch DDP) to replicate the model across workers for training on larger batches. Vertex AI distributed training coordinates the multi-worker setup, making this the only viable strategy for a model that exceeds single-GPU memory while requiring data parallelism.
What should I do if I get this PMLE 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: 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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