- A
The model architecture is too simple to benefit from distribution
Why wrong: Even simple models can scale; overhead is usually from communication.
- B
The workers are not using the same version of TensorFlow
Why wrong: Version mismatch would cause errors, not scaling issues.
- C
Communication overhead due to gradient synchronization
MultiWorkerMirroredStrategy synchronizes gradients across workers; network latency can limit scaling.
- D
The GPUs are not configured correctly
Why wrong: GPU misconfiguration would likely cause errors, not sublinear scaling.
Question 932 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.
A machine learning engineer is training a TensorFlow model on Vertex AI using distributed training with the MultiWorkerMirroredStrategy. The training job uses 4 workers with 4 GPUs each. The engineer notices that the training is not scaling linearly. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Communication overhead due to gradient synchronization
With MultiWorkerMirroredStrategy, each worker computes gradients independently on its local batch, then all-reduces gradients across workers via collective communication (e.g., NCCL or gRPC). As the number of workers increases, the communication overhead for gradient synchronization grows, often dominating the per-step time and preventing linear scaling. This is the most common bottleneck in distributed TensorFlow training, especially with many workers or small batch sizes per worker.
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.
- ✗
The model architecture is too simple to benefit from distribution
Why it's wrong here
Even simple models can scale; overhead is usually from communication.
- ✗
The workers are not using the same version of TensorFlow
Why it's wrong here
Version mismatch would cause errors, not scaling issues.
- ✓
Communication overhead due to gradient synchronization
Why this is correct
MultiWorkerMirroredStrategy synchronizes gradients across workers; network latency can limit scaling.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The GPUs are not configured correctly
Why it's wrong here
GPU misconfiguration would likely cause errors, not sublinear scaling.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume more workers always means linear speedup, ignoring the fixed overhead of gradient synchronization that becomes the dominant factor in distributed training.
Detailed technical explanation
How to think about this question
MultiWorkerMirroredStrategy uses the all-reduce algorithm (e.g., ring all-reduce) to aggregate gradients across workers, which has a latency cost proportional to the number of workers and the gradient size. In practice, the scaling efficiency can be estimated by Amdahl's Law: the serial fraction (communication) becomes the bottleneck as workers increase. For small models or large cluster sizes, the communication-to-computation ratio rises, and techniques like gradient compression or asynchronous training (e.g., ParameterServerStrategy) may be needed to improve scaling.
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
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: Communication overhead due to gradient synchronization — With MultiWorkerMirroredStrategy, each worker computes gradients independently on its local batch, then all-reduces gradients across workers via collective communication (e.g., NCCL or gRPC). As the number of workers increases, the communication overhead for gradient synchronization grows, often dominating the per-step time and preventing linear scaling. This is the most common bottleneck in distributed TensorFlow training, especially with many workers or small batch sizes per worker.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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