- A
Data parallelism using PyTorch DistributedDataParallel (DDP)
Why wrong: DDP replicates the model on each GPU; model must fit on one GPU.
- B
Horovod with allreduce
Why wrong: Horovod is for data parallelism.
- C
Model parallelism using pipeline parallelism
Splits layers across GPUs, allowing large models.
- D
Vertex AI distributed training with TF_CONFIG
Why wrong: TF_CONFIG is for TensorFlow, not PyTorch.
Question 662 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Your team is training a very large transformer model that does not fit on a single GPU. They are using Vertex AI custom training with PyTorch. Which distributed training approach 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
✓
Model parallelism using pipeline parallelism
When a transformer model is too large to fit on a single GPU, model parallelism (specifically pipeline parallelism) is required because it splits the model's layers across multiple devices, with each device holding a subset of the model's parameters. Data parallelism (DDP) replicates the entire model on each GPU, which fails if the model exceeds a single GPU's memory. Pipeline parallelism allows training very large models by partitioning the model into stages and passing activations and gradients sequentially between devices.
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.
- ✗
Data parallelism using PyTorch DistributedDataParallel (DDP)
Why it's wrong here
DDP replicates the model on each GPU; model must fit on one GPU.
- ✗
Horovod with allreduce
Why it's wrong here
Horovod is for data parallelism.
- ✓
Model parallelism using pipeline parallelism
Why this is correct
Splits layers across GPUs, allowing large models.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Vertex AI distributed training with TF_CONFIG
Why it's wrong here
TF_CONFIG is for TensorFlow, not PyTorch.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between data parallelism (which replicates the model) and model parallelism (which splits the model), and the trap here is that candidates assume any distributed training framework (like DDP or Horovod) can handle oversized models, ignoring the fundamental memory constraint that data parallelism cannot overcome.
Detailed technical explanation
How to think about this question
Pipeline parallelism in PyTorch is typically implemented via torch.distributed.pipeline.sync.Pipe, which partitions the model into micro-batches and schedules them across devices to reduce idle time (bubble overhead). A key subtlety is that pipeline parallelism often requires careful balancing of layer sizes across stages to minimize the pipeline bubble, and it is commonly combined with tensor parallelism (e.g., Megatron-LM) for extremely large models. In practice, training a 175B-parameter model like GPT-3 uses a hybrid of pipeline, tensor, and data parallelism across hundreds of GPUs.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Scaling Prototypes into ML Models — study guide chapter
Learn the concepts, then practise the questions
- →
Scaling Prototypes into ML Models practice questions
Targeted practice on this topic area only
- →
All PMLE questions
1,000 questions across all exam domains
- →
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
- →
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Automating and Orchestrating ML Pipelines practice questions
Practise PMLE questions linked to Automating and Orchestrating ML Pipelines.
Collaborating Within and Across Teams to Manage Data and Models practice questions
Practise PMLE questions linked to Collaborating Within and Across Teams to Manage Data and Models.
Serving and Scaling Models practice questions
Practise PMLE questions linked to Serving and Scaling Models.
Monitoring ML Solutions practice questions
Practise PMLE questions linked to Monitoring ML Solutions.
Architecting Low-Code ML Solutions practice questions
Practise PMLE questions linked to Architecting Low-Code ML Solutions.
Scaling Prototypes into ML Models practice questions
Practise PMLE questions linked to Scaling Prototypes into ML Models.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Model parallelism using pipeline parallelism — When a transformer model is too large to fit on a single GPU, model parallelism (specifically pipeline parallelism) is required because it splits the model's layers across multiple devices, with each device holding a subset of the model's parameters. Data parallelism (DDP) replicates the entire model on each GPU, which fails if the model exceeds a single GPU's memory. Pipeline parallelism allows training very large models by partitioning the model into stages and passing activations and gradients sequentially between devices.
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.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.