The answer is to enable gradient accumulation with steps of 4 or more. This resolves the out-of-memory error during fine-tuning by simulating a larger batch size without increasing the per-step GPU memory footprint; instead of updating model weights after every batch of 8, it accumulates gradients over multiple forward passes before performing a single backward pass, drastically reducing peak memory usage. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of memory optimization techniques for large language models like Llama 2, where the trap is confusing batch size reduction with gradient accumulation—simply lowering the batch size may hurt convergence, while gradient accumulation preserves effective batch size. Remember the mnemonic: “Accumulate, don’t amputate” to keep your batch size intact while slashing memory.
1Z0-1127 Fundamentals of Large Language Models Practice Question
This 1Z0-1127 practice question tests your understanding of fundamentals of large language models. 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 machine learning team is fine-tuning a 7B parameter Llama 2 model on a custom dataset of 10,000 documents using OCI Data Science and GPU instances. They encounter out-of-memory (OOM) errors during the fine-tuning process. They are using a batch size of 8 and a sequence length of 2048. They cannot increase the GPU memory. Which change should they prioritize to resolve the OOM?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable gradient accumulation with steps of 4 or more.
Option B is correct because enabling gradient accumulation allows the effective batch size to be maintained while reducing per-step memory usage. Option A changes the model entirely, Option C may not fix the memory issue, and Option D helps but may still OOM if the batch size is too high; gradient accumulation is more directly targeted.
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.
✓
Enable gradient accumulation with steps of 4 or more.
Why this is correct
Correct: Gradient accumulation reduces memory per step without changing effective batch size.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use mixed precision training (FP16).
Why it's wrong here
Incorrect: Mixed precision reduces memory but may still OOM; gradient accumulation is more effective for batch size issues.
✗
Reduce the model size by using a 3B parameter version.
Why it's wrong here
Incorrect: This requires re-fine-tuning and may not be feasible.
✗
Decrease the number of training epochs.
Why it's wrong here
Incorrect: Fewer epochs do not fix OOM; they may underfit.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which 1Z0-1127 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.
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable gradient accumulation with steps of 4 or more. — Option B is correct because enabling gradient accumulation allows the effective batch size to be maintained while reducing per-step memory usage. Option A changes the model entirely, Option C may not fix the memory issue, and Option D helps but may still OOM if the batch size is too high; gradient accumulation is more directly targeted.
What should I do if I get this 1Z0-1127 question wrong?
Identify which 1Z0-1127 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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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