- A
Batch size
Why wrong: Batch size affects memory but not per-token compute cost.
- B
Number of model parameters
More parameters increase compute and memory requirements.
- C
Maximum sequence length
Longer sequences increase attention computation and memory usage.
- D
Quantization bits
Why wrong: Quantization reduces cost, not increases.
- E
Dataset size
Why wrong: Dataset size affects total training time but not per-step cost.
Question 200 of 991
1Z0-1127 Fundamentals of Large Language Models Practice Question
This 1Z0-1127 practice question tests your understanding of fundamentals of large language models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Which TWO factors most significantly influence the computational cost of fine-tuning a large language model?
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
✓
Number of model parameters
The number of model parameters directly determines the size of the weight matrices that must be updated during backpropagation. Fine-tuning requires storing gradients and optimizer states for each parameter, so the memory and compute scale linearly with parameter count. This is the primary driver of FLOPs (floating-point operations) per training step.
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.
- ✗
Batch size
Why it's wrong here
Batch size affects memory but not per-token compute cost.
- ✓
Number of model parameters
Why this is correct
More parameters increase compute and memory requirements.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Maximum sequence length
Why this is correct
Longer sequences increase attention computation and memory usage.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Quantization bits
Why it's wrong here
Quantization reduces cost, not increases.
- ✗
Dataset size
Why it's wrong here
Dataset size affects total training time but not per-step cost.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often confuse factors affecting per-step computational cost (parameters, sequence length) with those affecting total training time or memory efficiency (batch size, quantization, dataset size), leading to incorrect selections.
Detailed technical explanation
How to think about this question
The computational cost per training step is dominated by matrix multiplications in the transformer layers, which scale as O(L * d^2) where L is sequence length and d is hidden dimension (proportional to parameter count). For a model with N parameters, the backward pass requires roughly 2x the FLOPs of the forward pass, and optimizer states (e.g., Adam) add 8 bytes per parameter in mixed precision. In practice, sequence length also impacts the attention mechanism's O(L^2) complexity, making it a secondary but significant factor for long-context fine-tuning.
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 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.
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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: Number of model parameters — The number of model parameters directly determines the size of the weight matrices that must be updated during backpropagation. Fine-tuning requires storing gradients and optimizer states for each parameter, so the memory and compute scale linearly with parameter count. This is the primary driver of FLOPs (floating-point operations) per training step.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
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 →
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Last reviewed: Jul 4, 2026
This 1Z0-1127 practice question is part of Courseiva's free Oracle 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 1Z0-1127 exam.
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