- A
Configure TF_CONFIG for distributed training
When using TPU pods with multi-worker, TF_CONFIG must be set to coordinate workers.
- B
Set the training worker pool to use only one worker
Why wrong: TPU pod slices often require multiple workers for best performance, but the question asks for required actions. One worker can use a TPU pod, but it's not a requirement.
- C
Specify the accelerator type as TPU_V3 and topology as '2x2x4'
For a v3-32 pod slice, topology '2x2x4' is correct (32 TPU chips).
- D
Use a custom container with TensorFlow 2.12 and TPU support
Why wrong: A custom container is not necessary because Vertex AI's pre-built TensorFlow containers already support TPU training. Using a pre-built container simplifies setup and is the recommended approach.
- E
Set the machine type to a high-memory VM with NVIDIA A100 GPUs
Why wrong: TPU training does not use GPUs; the accelerator type should be TPU.
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.
A team is training a custom TensorFlow model on Vertex AI using a pre-built container. They need to use a TPU pod slice (v3-32). What THREE actions are required to set up the training job correctly?
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
Configure TF_CONFIG for distributed training
Options A and C are correct because TPU pod slices on Vertex AI require setting TF_CONFIG for distributed training (A) and specifying the accelerator type as TPU_V3 with the appropriate topology (C). Option D is not required: Vertex AI provides pre-built containers for TensorFlow that already include TPU support, so a custom container is unnecessary. Using a custom container would be an extra step not needed for this scenario.
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.
- ✓
Configure TF_CONFIG for distributed training
Why this is correct
When using TPU pods with multi-worker, TF_CONFIG must be set to coordinate workers.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set the training worker pool to use only one worker
Why it's wrong here
TPU pod slices often require multiple workers for best performance, but the question asks for required actions. One worker can use a TPU pod, but it's not a requirement.
- ✓
Specify the accelerator type as TPU_V3 and topology as '2x2x4'
Why this is correct
For a v3-32 pod slice, topology '2x2x4' is correct (32 TPU chips).
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a custom container with TensorFlow 2.12 and TPU support
Why it's wrong here
A custom container is not necessary because Vertex AI's pre-built TensorFlow containers already support TPU training. Using a pre-built container simplifies setup and is the recommended approach.
- ✗
Set the machine type to a high-memory VM with NVIDIA A100 GPUs
Why it's wrong here
TPU training does not use GPUs; the accelerator type should be TPU.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that TPU pods can be treated as a single accelerator like a GPU, leading candidates to select a single-worker pool (Option B) or a GPU machine type (Option E), when in fact TPU pod slices require explicit multi-worker topology and TF_CONFIG setup.
Detailed technical explanation
How to think about this question
Under the hood, a TPU v3-32 pod slice has a 2x2x4 toroidal mesh topology (4 hosts, 8 cores each). Vertex AI maps this to 4 worker replicas, each running a TensorFlow process that communicates via the TPU's high-speed interconnect (ICI). The `TF_CONFIG` must set `"cluster":{"worker":["host1:port","host2:port","host3:port","host4:port"]}` and `"task":{"type":"worker","index":<0-3>}`. A common real-world pitfall is omitting the `chief` job or misconfiguring the port, which causes the workers to hang during `tf.distribute.TPUStrategy` initialization.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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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: Configure TF_CONFIG for distributed training — Options A and C are correct because TPU pod slices on Vertex AI require setting TF_CONFIG for distributed training (A) and specifying the accelerator type as TPU_V3 with the appropriate topology (C). Option D is not required: Vertex AI provides pre-built containers for TensorFlow that already include TPU support, so a custom container is unnecessary. Using a custom container would be an extra step not needed for this scenario.
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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