- A
Model parallelism using pipeline parallelism
Pipeline parallelism splits layers across devices, allowing large models to fit by distributing the model parameters.
- B
Data parallelism with MultiWorkerMirroredStrategy
Why wrong: Same as A but across machines; still replicates model, not suitable if model doesn't fit per device.
- C
Mixed precision training (FP16)
Why wrong: Mixed precision halves memory but still requires the model to fit on one GPU; 7B may still not fit.
- D
Data parallelism with tf.distribute.MirroredStrategy
Why wrong: Data parallelism replicates the entire model on each GPU, requiring each GPU to hold the model, which is not possible if model doesn't fit.
Question 399 of 1,000
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.
You are fine-tuning a large language model (LLM) from Hugging Face Transformers using Vertex AI Training. The model has 7 billion parameters and does not fit into the memory of a single GPU. You need to train across multiple GPUs, splitting the model layers across devices. Which distributed training approach should you 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
Model parallelism (pipeline parallelism) splits model layers across devices, necessary for large models that don't fit on one GPU. Data parallelism replicates the model and splits data, not suitable if model doesn't fit. Mixed precision reduces memory but still requires model parallelism for 7B. Fully sharded data parallelism (FSDP) is a form of data parallelism with sharding, but pipeline parallelism is more common for layer-wise splitting.
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.
- ✓
Model parallelism using pipeline parallelism
Why this is correct
Pipeline parallelism splits layers across devices, allowing large models to fit by distributing the model parameters.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Data parallelism with MultiWorkerMirroredStrategy
Why it's wrong here
Same as A but across machines; still replicates model, not suitable if model doesn't fit per device.
- ✗
Mixed precision training (FP16)
Why it's wrong here
Mixed precision halves memory but still requires the model to fit on one GPU; 7B may still not fit.
- ✗
Data parallelism with tf.distribute.MirroredStrategy
Why it's wrong here
Data parallelism replicates the entire model on each GPU, requiring each GPU to hold the model, which is not possible if model doesn't fit.
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.
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 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 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.
- →
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 — Model parallelism (pipeline parallelism) splits model layers across devices, necessary for large models that don't fit on one GPU. Data parallelism replicates the model and splits data, not suitable if model doesn't fit. Mixed precision reduces memory but still requires model parallelism for 7B. Fully sharded data parallelism (FSDP) is a form of data parallelism with sharding, but pipeline parallelism is more common for layer-wise splitting.
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 →
Keep practising
More PMLE practice questions
- A travel booking company has a real-time recommendation system that suggests hotels and flights to users. The model is s…
- A global retail company uses Vertex AI Recommendations to provide product recommendations on their website. They have a…
- Your team is developing a machine learning model for real-time fraud detection. The training pipeline runs on Vertex AI…
- A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive…
- You are an ML engineer at a global e-commerce company. Your team has developed a deep learning model for product recomme…
- A financial services company uses Vertex AI AutoML Tables to build a credit risk model. The dataset contains 500,000 row…
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.