Quick Answer
The correct action is to reduce the batch size, as this directly resolves out-of-memory errors during fine-tuning on Vertex AI. When you lower the batch size, each training step processes fewer samples, which proportionally decreases the memory footprint required to store activations and gradients, thereby preventing the OOM crash. On the Google Cloud Generative AI Leader exam, this question tests your understanding of resource optimization under memory constraints—a common trap is assuming that more powerful hardware or larger models solve memory issues, when in fact they exacerbate them. Remember that TPUs and GPUs both have finite high-bandwidth memory; reducing batch size is the universal first step to resolve out-of-memory errors in fine-tuning, regardless of accelerator type. For a quick memory tip: think “smaller batches, bigger chances”—cutting the batch size is the leanest fix for an OOM error.
This Generative AI Leader practice question tests your understanding of fundamentals of generative ai. 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 data scientist is fine-tuning a large language model using Vertex AI. The training job fails with an out-of-memory error. Which action should they take to resolve this issue?
Change the accelerator to TPU
Why wrong: TPU still has memory limits; reducing batch size is more direct.
Use a larger model
Why wrong: Larger models require more memory.
Increase the batch size
Why wrong: Increasing batch size increases memory usage.
Reduce the batch size
Smaller batch size reduces memory footprint.
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
Reducing batch size lowers memory consumption per step, directly addressing OOM. Option A is wrong because increasing batch size worsens memory usage. Option B is wrong because switching to a larger model increases memory. Option D is wrong because TPUs also have memory limits; reducing batch size works on TPU as well.
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.
Change the accelerator to TPU
Why it's wrong here
TPU still has memory limits; reducing batch size is more direct.
Use a larger model
Why it's wrong here
Larger models require more memory.
Increase the batch size
Why it's wrong here
Increasing batch size increases memory usage.
Reduce the batch size
Why this is correct
Smaller batch size reduces memory footprint.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
The correct answer is: Reduce the batch size — Reducing batch size lowers memory consumption per step, directly addressing OOM. Option A is wrong because increasing batch size worsens memory usage. Option B is wrong because switching to a larger model increases memory. Option D is wrong because TPUs also have memory limits; reducing batch size works on TPU as well.
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 22, 2026
This Generative AI Leader 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 Generative AI Leader exam.
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