- A
Reduce the number of output tokens requested.
Correct: Fewer tokens means faster generation.
- B
Increase the max tokens parameter.
Why wrong: Incorrect: More tokens increase latency.
- C
Switch to a fine-tuned version of the same model.
Why wrong: Incorrect: Fine-tuning doesn't significantly affect latency.
- D
Enable batched inference.
Why wrong: Incorrect: Batching improves throughput, not per-request latency.
Question 113 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.
An application using OCI GenAI experiences high response times. Which change will most directly reduce latency?
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
✓
Reduce the number of output tokens requested.
Reducing the number of output tokens requested directly decreases the amount of text the model must generate per request. Since latency in OCI GenAI is proportional to the total number of tokens processed (input + output), fewer output tokens mean fewer autoregressive decoding steps, which directly reduces response time.
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.
- ✓
Reduce the number of output tokens requested.
Why this is correct
Correct: Fewer tokens means faster generation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the max tokens parameter.
Why it's wrong here
Incorrect: More tokens increase latency.
- ✗
Switch to a fine-tuned version of the same model.
Why it's wrong here
Incorrect: Fine-tuning doesn't significantly affect latency.
- ✗
Enable batched inference.
Why it's wrong here
Incorrect: Batching improves throughput, not per-request latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle OCI GenAI often tests the misconception that increasing max tokens or using fine-tuned models improves speed, when in fact these changes either increase computational load or have no effect on per-request latency.
Detailed technical explanation
How to think about this question
Under the hood, autoregressive LLMs generate tokens one at a time, with each token requiring a forward pass through the transformer. The total latency is roughly (input tokens + output tokens) * time per token, so reducing output tokens has a linear effect on response time. In OCI GenAI, the time per token is dominated by memory bandwidth and attention computation, making output token count a primary lever for latency optimization.
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: Reduce the number of output tokens requested. — Reducing the number of output tokens requested directly decreases the amount of text the model must generate per request. Since latency in OCI GenAI is proportional to the total number of tokens processed (input + output), fewer output tokens mean fewer autoregressive decoding steps, which directly reduces response time.
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.
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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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