- A
Model parallelism using tf.distribute.experimental.PipelineMirroredStrategy
PipelineMirroredStrategy implements pipeline parallelism, which splits the model across GPUs, reducing per-device memory footprint. This is appropriate for models too large for a single GPU.
- B
Data parallelism using tf.distribute.MirroredStrategy
Why wrong: Data parallelism replicates the model on each GPU, requiring the entire model to fit on one GPU. It does not help when the model is too large for a single GPU.
- C
Multi-worker mirrored strategy with a single worker per node
Why wrong: Multi-worker mirrored strategy is a form of data parallelism across nodes, still requiring the model to fit on each GPU.
- D
Hyperparameter tuning with Vertex AI Vizier
Why wrong: Hyperparameter tuning optimizes hyperparameters, not model parallelism. It does not address the memory limitation.
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.
A machine learning team is training a large transformer model on Vertex AI. They need to reduce training time by utilizing multiple GPUs across nodes, but the model is too large to fit into a single GPU memory. Which distributed training 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
Model parallelism using tf.distribute.experimental.PipelineMirroredStrategy
Option A is correct because PipelineMirroredStrategy combines model parallelism (splitting the transformer layers across multiple GPUs) with pipeline parallelism to handle models that exceed single GPU memory. This strategy partitions the model into stages, each placed on a different GPU, and uses micro-batching to keep all GPUs busy, which is essential for large transformer models that cannot fit into a single GPU's memory.
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 tf.distribute.experimental.PipelineMirroredStrategy
Why this is correct
PipelineMirroredStrategy implements pipeline parallelism, which splits the model across GPUs, reducing per-device memory footprint. This is appropriate for models too large for a single GPU.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Data parallelism using tf.distribute.MirroredStrategy
Why it's wrong here
Data parallelism replicates the model on each GPU, requiring the entire model to fit on one GPU. It does not help when the model is too large for a single GPU.
- ✗
Multi-worker mirrored strategy with a single worker per node
Why it's wrong here
Multi-worker mirrored strategy is a form of data parallelism across nodes, still requiring the model to fit on each GPU.
- ✗
Hyperparameter tuning with Vertex AI Vizier
Why it's wrong here
Hyperparameter tuning optimizes hyperparameters, not model parallelism. It does not address the memory limitation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse data parallelism (which requires the model to fit in a single GPU) with model parallelism, and assume that multi-worker strategies inherently solve memory constraints, but they only distribute data, not the model itself.
Detailed technical explanation
How to think about this question
PipelineMirroredStrategy uses gradient accumulation across micro-batches to pipeline execution, reducing idle time (bubble overhead) compared to naive model parallelism. In practice, for transformer models like GPT-3, this strategy can be combined with tensor parallelism (e.g., using NVIDIA's Megatron-LM) to further split individual layers across GPUs. The strategy leverages NCCL for efficient inter-GPU communication and supports automatic partitioning via tf.distribute.experimental.PipelineMirroredStrategy.
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: Model parallelism using tf.distribute.experimental.PipelineMirroredStrategy — Option A is correct because PipelineMirroredStrategy combines model parallelism (splitting the transformer layers across multiple GPUs) with pipeline parallelism to handle models that exceed single GPU memory. This strategy partitions the model into stages, each placed on a different GPU, and uses micro-batching to keep all GPUs busy, which is essential for large transformer models that cannot fit into a single GPU's memory.
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 →
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Last reviewed: Jul 4, 2026
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