Question 987 of 991

Fundamentals of Large Language Models →hardMultiple ChoiceObjective-mapped

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?

A
Enable gradient accumulation with steps of 4 or more.
Correct. Gradient accumulation reduces per-step memory by updating weights after several forward/backward passes, effectively simulating a larger batch size without increasing GPU memory usage per step.
B
Use mixed precision training (FP16).
Why wrong: Incorrect. Mixed precision training (FP16) can reduce memory usage and speed up training, but it may not be sufficient to resolve OOM if the batch size and sequence length are already near the limit. It is a helpful technique but not the primary fix.
C
Reduce the model size by using a 3B parameter version.
Why wrong: Incorrect. Reducing model size (e.g., from 7B to 3B parameters) would lower memory requirements, but it is a significant change that alters the model's capacity and may not be necessary if gradient accumulation can solve the issue.
D
Decrease the number of training epochs.
Why wrong: Incorrect. Decreasing the number of training epochs does not affect the memory usage per step; it only reduces total training time, so OOM errors would still occur.

Full breakdown with real-world context →

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

✓

Enable gradient accumulation with steps of 4 or more.

Option A is correct because gradient accumulation divides the batch into micro-batches, reducing per-step memory usage while maintaining the effective batch size. This directly addresses OOM errors without increasing GPU memory. Option B (mixed precision) can reduce memory but may not be sufficient with a batch size of 8 and sequence length 2048. Option C changes the model, which may not be necessary or feasible. Option D (reducing epochs) does not affect per-step memory and may still cause OOM.

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 per-step memory by updating weights after several forward/backward passes, effectively simulating a larger batch size without increasing GPU memory usage per step.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use mixed precision training (FP16).
Why it's wrong here
Incorrect. Mixed precision training (FP16) can reduce memory usage and speed up training, but it may not be sufficient to resolve OOM if the batch size and sequence length are already near the limit. It is a helpful technique but not the primary fix.
✗
Reduce the model size by using a 3B parameter version.
Why it's wrong here
Incorrect. Reducing model size (e.g., from 7B to 3B parameters) would lower memory requirements, but it is a significant change that alters the model's capacity and may not be necessary if gradient accumulation can solve the issue.
✗
Decrease the number of training epochs.
Why it's wrong here
Incorrect. Decreasing the number of training epochs does not affect the memory usage per step; it only reduces total training time, so OOM errors would still occur.

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.

Visual reference

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.

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FAQ

Questions learners often ask

What does this 1Z0-1127 question test?

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 A is correct because gradient accumulation divides the batch into micro-batches, reducing per-step memory usage while maintaining the effective batch size. This directly addresses OOM errors without increasing GPU memory. Option B (mixed precision) can reduce memory but may not be sufficient with a batch size of 8 and sequence length 2048. Option C changes the model, which may not be necessary or feasible. Option D (reducing epochs) does not affect per-step memory and may still cause OOM.

What should I do if I get this 1Z0-1127 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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