- A
Too few training steps
Why wrong: Number of steps does not cause OOM.
- B
Batch size too large
Why wrong: While possible, the machine type is the primary consideration; batch size can be adjusted.
- C
Dataset too small
Why wrong: Dataset size does not cause OOM during training.
- D
Wrong machine type with insufficient memory
Insufficient GPU or TPU memory leads to OOM; selecting a larger machine type often resolves it.
Question 127 of 997
Why Does Fine-Tuning Fail with 'ResourceExhausted: Out of Memory'?
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.
During fine-tuning a model on Vertex AI, the job fails with error 'ResourceExhausted: Out of memory'. 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.
Quick Answer
The answer is a wrong machine type with insufficient memory. This is the most likely cause because the model’s parameter count, combined with your chosen batch size and sequence length, directly determines the total GPU or TPU memory required; when that demand exceeds the machine’s available RAM, Vertex AI throws the ResourceExhausted error. On the Google Cloud Generative AI Leader exam, this question tests your understanding of how hardware provisioning impacts training stability—a common trap is assuming the error stems from data size or code bugs rather than resource allocation. Remember the memory tip: “Model + Batch = Math; if the sum exceeds the machine, you’ll crash the cache.” Always verify your machine type supports the model’s footprint before launching a fine-tuning job.
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
✓
Wrong machine type with insufficient memory
The error 'ResourceExhausted: Out of memory' during fine-tuning on Vertex AI indicates that the allocated memory on the chosen machine type is insufficient to hold the model weights, gradients, optimizer states, and the input batch simultaneously. Option D is correct because selecting a machine type with inadequate RAM (e.g., using a standard n1-standard-4 instead of a high-memory machine like n1-highmem-16) directly causes this out-of-memory condition, especially for large foundation models like PaLM or Llama 2.
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.
- ✗
Too few training steps
Why it's wrong here
Number of steps does not cause OOM.
- ✗
Batch size too large
Why it's wrong here
While possible, the machine type is the primary consideration; batch size can be adjusted.
- ✗
Dataset too small
Why it's wrong here
Dataset size does not cause OOM during training.
- ✓
Wrong machine type with insufficient memory
Why this is correct
Insufficient GPU or TPU memory leads to OOM; selecting a larger machine type often resolves it.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap in Google Cloud AI exams is assuming that OOM errors are solely due to batch size. However, the root cause is often the machine type's memory capacity—Vertex AI provides specific machine series (e.g., n1-highmem, a2-highgpu) optimized for large models. Even with a modest batch size, using a low-memory machine like n1-standard-4 can trigger 'ResourceExhausted' on large foundation models.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI fine-tuning loads the base model into GPU/TPU memory, then allocates additional memory for gradients and optimizer states (e.g., Adam optimizer stores two additional states per parameter). For a 7B-parameter model in 32-bit precision, this requires roughly 28 GB just for weights, plus 56 GB for optimizer states, totaling over 84 GB—far exceeding the 16 GB of a standard n1-standard-4 machine. A real-world scenario is fine-tuning Llama 2 13B on Vertex AI: if you select an n1-standard-8 (30 GB RAM) instead of an n1-highmem-32 (208 GB RAM), the job fails with this exact error because the memory cannot accommodate the model and its training overhead.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Wrong machine type with insufficient memory — The error 'ResourceExhausted: Out of memory' during fine-tuning on Vertex AI indicates that the allocated memory on the chosen machine type is insufficient to hold the model weights, gradients, optimizer states, and the input batch simultaneously. Option D is correct because selecting a machine type with inadequate RAM (e.g., using a standard n1-standard-4 instead of a high-memory machine like n1-highmem-16) directly causes this out-of-memory condition, especially for large foundation models like PaLM or Llama 2.
What should I do if I get this Generative AI Leader 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.
About these practice questions
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Last reviewed: Jul 4, 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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