- A
A startup script to configure the TPU pod (e.g., `xla_lib.sh`)
Startup scripts are often needed to initialize TPU devices.
- B
A MultiWorkerMirroredStrategy configuration
Why wrong: This is for TensorFlow data parallelism, not PyTorch TPU.
- C
A Docker image that includes PyTorch and the TPU library (torch-xla)
The container must have the necessary dependencies for TPU training.
- D
A TF_CONFIG environment variable set for each worker
Why wrong: TF_CONFIG is for TensorFlow distributed training, not PyTorch.
- E
A CustomJob with a TPU accelerator type (e.g., v3-32)
TPU training requires specifying the TPU type in the job.
Question 177 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
An ML engineer is using Vertex AI for distributed training of a PyTorch model across multiple nodes. The training job must use TPUs for high throughput. The engineer sets up the job configuration. Which THREE components are required for the training to work correctly? (Select 3)
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
✓
A startup script to configure the TPU pod (e.g., `xla_lib.sh`)
A is correct because TPU pods require a startup script (e.g., `xla_lib.sh`) to initialize the XLA runtime, configure the TPU mesh, and set environment variables like `XRT_TPU_CONFIG`. Without this script, the TPU devices will not be discoverable by the PyTorch/XLA process, causing the training to fail with device-not-found errors.
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.
- ✓
A startup script to configure the TPU pod (e.g., `xla_lib.sh`)
Why this is correct
Startup scripts are often needed to initialize TPU devices.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A MultiWorkerMirroredStrategy configuration
Why it's wrong here
This is for TensorFlow data parallelism, not PyTorch TPU.
- ✓
A Docker image that includes PyTorch and the TPU library (torch-xla)
Why this is correct
The container must have the necessary dependencies for TPU training.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A TF_CONFIG environment variable set for each worker
Why it's wrong here
TF_CONFIG is for TensorFlow distributed training, not PyTorch.
- ✓
A CustomJob with a TPU accelerator type (e.g., v3-32)
Why this is correct
TPU training requires specifying the TPU type in the job.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between TensorFlow and PyTorch distributed training configurations, and the trap here is assuming that `TF_CONFIG` or `MultiWorkerMirroredStrategy` are universal for all frameworks, when in fact PyTorch uses its own environment variables and the `torch-xla` library for TPU training.
Detailed technical explanation
How to think about this question
Under the hood, PyTorch/XLA relies on the XRT (XLA Runtime) server running on each TPU host; the startup script launches this server and sets `XRT_TPU_CONFIG` to point to the local TPU worker. In a multi-node TPU pod (e.g., v3-32), each node must have its own XRT server and coordinate via `torch.distributed` using the `xla` backend, which uses gRPC for collective communication. A real-world pitfall is forgetting to set `XRT_DEVICE_MAP` and `XRT_WORKERS` for multi-host setups, leading to partial device visibility.
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: A startup script to configure the TPU pod (e.g., `xla_lib.sh`) — A is correct because TPU pods require a startup script (e.g., `xla_lib.sh`) to initialize the XLA runtime, configure the TPU mesh, and set environment variables like `XRT_TPU_CONFIG`. Without this script, the TPU devices will not be discoverable by the PyTorch/XLA process, causing the training to fail with device-not-found errors.
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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