- A
Use greedy decoding instead of sampling
Why wrong: Greedy decoding does not reduce cost significantly.
- B
Use a smaller model from the same family
Smaller models are cheaper to run.
- C
Increase max tokens to ensure complete answers
Why wrong: More tokens increase cost.
- D
Implement caching for repeated queries
Caching avoids redundant computation.
- E
Optimize prompts to be more concise
Shorter prompts reduce token usage.
Question 210 of 991
1Z0-1127 LLM Fundamentals Practice Question
This 1Z0-1127 practice question tests your understanding of llm fundamentals. 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 OCI user wants to reduce the cost of running a generative AI model while maintaining output quality. Which THREE strategies can help achieve this?
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
✓
Use a smaller model from the same family
Option B is correct because using a smaller model from the same family (e.g., switching from Llama 3 70B to Llama 3 8B) reduces the number of parameters and computational resources required per inference, directly lowering cost. Smaller models often retain strong performance on many tasks, especially when the task complexity does not demand the full capacity of the larger model, thus maintaining output quality while reducing token processing costs.
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.
- ✗
Use greedy decoding instead of sampling
Why it's wrong here
Greedy decoding does not reduce cost significantly.
- ✓
Use a smaller model from the same family
Why this is correct
Smaller models are cheaper to run.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase max tokens to ensure complete answers
Why it's wrong here
More tokens increase cost.
- ✓
Implement caching for repeated queries
Why this is correct
Caching avoids redundant computation.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Optimize prompts to be more concise
Why this is correct
Shorter prompts reduce token usage.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that greedy decoding reduces cost (it does not—it only changes the decoding strategy, not the model size or token count) and that increasing max tokens improves quality (it actually increases cost and can degrade output by encouraging rambling).
Detailed technical explanation
How to think about this question
Under the hood, model cost is proportional to the number of parameters and the number of tokens processed (both input and output). Smaller models have fewer floating-point operations (FLOPs) per token, so switching from a 70B to an 8B model reduces FLOPs by roughly an order of magnitude. Caching repeated queries (Option D) avoids recomputing key-value (KV) cache for identical input prefixes, reducing latency and cost for frequent prompts. Concise prompts (Option E) reduce input token count, directly lowering per-request cost and often improving focus, as models tend to perform better with clear, succinct instructions.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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LLM Fundamentals — study guide chapter
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
LLM Fundamentals — This question tests LLM Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a smaller model from the same family — Option B is correct because using a smaller model from the same family (e.g., switching from Llama 3 70B to Llama 3 8B) reduces the number of parameters and computational resources required per inference, directly lowering cost. Smaller models often retain strong performance on many tasks, especially when the task complexity does not demand the full capacity of the larger model, thus maintaining output quality while reducing token processing costs.
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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