Quick Answer
The correct first action is to reduce the batch size in the training script. This directly addresses the Vertex AI training out of memory error by shrinking the memory footprint required for storing intermediate activations and gradients during backpropagation, which is the most immediate and cost-effective fix without altering the underlying n1-standard-4 VM with 15 GB RAM. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of memory bottlenecks in distributed training—a common trap is to immediately suggest upgrading the machine type or adding more GPUs, but the exam emphasizes optimizing hyperparameters before infrastructure changes. Remember the memory tip: "Batch size down, memory free—scale up only when you must."
This PMLE practice question tests your understanding of solving business challenges with ml. 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 company is training a large neural network on Vertex AI and training jobs keep failing with 'Out of memory' errors. The VM uses a standard n1-standard-4 machine with 15 GB RAM. Which action should they take first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Use a larger machine type like n1-standard-16
Why wrong: Fixes symptom but costlier; batch size should be tuned first.
Reduce the batch size in the training script
Smaller batch size reduces peak memory usage.
Enable distributed training across multiple VMs
Why wrong: Adds complexity; OOM usually on a single VM.
Switch the training to CPU only
Why wrong: CPU training still requires memory; may not fix OOM.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Reduce the batch size in the training script
The 'Out of memory' error on a n1-standard-4 VM (15 GB RAM) indicates the model's memory footprint exceeds available RAM. Reducing the batch size directly decreases the memory required for storing intermediate activations and gradients during training, which is the most immediate and cost-effective fix without changing the underlying infrastructure.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use a larger machine type like n1-standard-16
Why it's wrong here
Fixes symptom but costlier; batch size should be tuned first.
Reduce the batch size in the training script
Why this is correct
Smaller batch size reduces peak memory usage.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Enable distributed training across multiple VMs
Why it's wrong here
Adds complexity; OOM usually on a single VM.
Switch the training to CPU only
Why it's wrong here
CPU training still requires memory; may not fix OOM.
Common exam traps
The trap here is that candidates often jump to scaling up infrastructure (larger machine or distributed training) instead of first tuning the training hyperparameter (batch size) that directly controls memory consumption, which is the simplest and most cost-effective fix.
Detailed technical explanation
The OOM error occurs because the forward pass computes and stores activations for each layer, and the memory required scales linearly with batch size. Reducing batch size by a factor of 2 halves the memory for activations, gradients, and optimizer states. In practice, you can monitor memory usage with `nvidia-smi` (if GPUs were present) or `free -m` on the VM, and use a binary search on batch size to find the maximum that fits, often starting with a batch size of 1 and doubling until OOM.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Solving business challenges with ML — study guide chapter
Learn the concepts, then practise the questions
Solving business challenges with ML practice questions
Targeted practice on this topic area only
All PMLE questions
506 questions across all exam domains
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise PMLE questions linked to Scaling prototypes into ML models.
Practise PMLE questions linked to Automating and orchestrating ML pipelines.
Practise PMLE questions linked to Collaborating within and across teams to manage data and models.
Practise PMLE questions linked to Architecting low-code ML solutions.
Practise PMLE questions linked to Collaborating to manage data and models.
Practise PMLE questions linked to Serving and scaling models.
Practise PMLE questions linked to Monitoring ML solutions.
Practise PMLE questions linked to Solving business challenges with ML.
Practise PMLE questions linked to PMLE fundamentals.
Practise PMLE questions linked to PMLE scenario.
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
The correct answer is: Reduce the batch size in the training script — The 'Out of memory' error on a n1-standard-4 VM (15 GB RAM) indicates the model's memory footprint exceeds available RAM. Reducing the batch size directly decreases the memory required for storing intermediate activations and gradients during training, which is the most immediate and cost-effective fix without changing the underlying infrastructure.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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 →
Last reviewed: Jun 24, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.