- A
Use Cloud TPU Pods for distributed training
Why wrong: TPU Pods are different from GPU training.
- B
Use Dataflow for distributed training
Why wrong: Dataflow is for data processing, not training.
- C
Use Vertex AI Training with a custom job specifying workerPoolSpecs and MultiWorkerMirroredStrategy
MultiWorkerMirroredStrategy distributes across multiple machines.
- D
Use a single worker with multiple GPUs and TensorFlow MirroredStrategy
Why wrong: MirroredStrategy is single-machine multi-GPU, not multi-machine.
Question 968 of 1,000
Multi-GPU Multi-Machine Distributed Training with Vertex AI Custom Containers
This PDE practice question tests your understanding of operationalizing machine learning 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 team is training a large model using a custom container with TensorFlow on Vertex AI Training. They need to use multiple GPUs across several machines. Which strategy should they implement to maximize training throughput?
Quick Answer
The correct choice is to use Vertex AI Training with a custom job specifying workerPoolSpecs and the MultiWorkerMirroredStrategy. This is because MultiWorkerMirroredStrategy is TensorFlow’s native API for synchronous distributed training across multiple machines, each equipped with multiple GPUs, enabling the model to scale beyond the memory and compute limits of a single VM. On the Google Professional Data Engineer exam, this scenario tests your understanding of Vertex AI’s distributed training architecture, where workerPoolSpecs define the cluster topology (chief and workers) and the distribution_strategy argument activates multi-machine parallelism. A common trap is confusing MirroredStrategy (single-machine, multi-GPU) with MultiWorkerMirroredStrategy; the exam often presents a scenario requiring cross-machine scaling to test this distinction. Memory tip: think “MultiWorker” for multi-machine, “Mirrored” for one machine—if you need to split across boxes, you need the “Worker” in the name.
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 Vertex AI Training with a custom job specifying workerPoolSpecs and MultiWorkerMirroredStrategy
Option C is correct because Vertex AI Training's custom job with workerPoolSpecs enables multi-machine, multi-GPU distributed training, and TensorFlow's MultiWorkerMirroredStrategy is specifically designed for synchronous distributed training across multiple workers. This combination maximizes throughput by efficiently synchronizing gradients across all GPUs on all machines using all-reduce communication, which is essential for large model training.
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 Cloud TPU Pods for distributed training
Why it's wrong here
TPU Pods are different from GPU training.
- ✗
Use Dataflow for distributed training
Why it's wrong here
Dataflow is for data processing, not training.
- ✓
Use Vertex AI Training with a custom job specifying workerPoolSpecs and MultiWorkerMirroredStrategy
Why this is correct
MultiWorkerMirroredStrategy distributes across multiple machines.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a single worker with multiple GPUs and TensorFlow MirroredStrategy
Why it's wrong here
MirroredStrategy is single-machine multi-GPU, not multi-machine.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the distinction between single-machine multi-GPU strategies (MirroredStrategy) and multi-machine distributed strategies (MultiWorkerMirroredStrategy), leading candidates to pick D when they overlook the requirement for multiple machines.
Detailed technical explanation
How to think about this question
MultiWorkerMirroredStrategy uses TensorFlow's collective ops (e.g., all-reduce via NCCL) to synchronize gradients across workers, with each worker managing its own set of GPUs. Vertex AI's workerPoolSpecs allow defining separate machine configurations for chief and parameter servers (if needed), but for synchronous training, the chief worker coordinates the all-reduce. In practice, setting `TF_CONFIG` environment variable correctly is critical for worker discovery and communication, and using a high-speed network like VPC with GPUDirect-TCPX can further reduce communication overhead.
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.
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FAQ
Questions learners often ask
What does this PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Vertex AI Training with a custom job specifying workerPoolSpecs and MultiWorkerMirroredStrategy — Option C is correct because Vertex AI Training's custom job with workerPoolSpecs enables multi-machine, multi-GPU distributed training, and TensorFlow's MultiWorkerMirroredStrategy is specifically designed for synchronous distributed training across multiple workers. This combination maximizes throughput by efficiently synchronizing gradients across all GPUs on all machines using all-reduce communication, which is essential for large model training.
What should I do if I get this PDE 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
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