Quick Answer
The correct adjustment is to add constraints like “Max 30 words. Focus on key features.” This works because prompt engineering with explicit constraints directly instructs the LLM to limit verbosity and prioritize relevant details, effectively reducing verbosity in LLM output via prompt constraints without altering model parameters or relying on examples that may not generalize. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this tests your understanding of controlling generation behavior through prompt design rather than fine-tuning or post-processing—a common trap is assuming longer prompts or more examples always improve output, when in fact concise, directive constraints are more reliable. Remember the mnemonic “CLEAR”: Constraints, Length, Emphasis, Action, Relevance—each element forces the model to stay on point.
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.
A company uses an LLM to generate product descriptions. The outputs are consistently too verbose and include irrelevant details. The prompt includes a simple instruction: 'Describe the product.' Which adjustment to the prompt is most likely to yield concise, relevant descriptions?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Set temperature to 0.
Why wrong: Low temperature reduces randomness but does not enforce brevity.
Increase max_tokens to 500.
Why wrong: More tokens encourage longer outputs, not shorter.
Add constraints like 'Max 30 words. Focus on key features.'
Explicit constraints directly limit length and scope.
Include a few examples of desired short descriptions.
Why wrong: Examples help but without explicit constraints, model may still be verbose.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Add constraints like 'Max 30 words. Focus on key features.'
Option C is correct because adding explicit constraints like 'Max 30 words. Focus on key features.' directly instructs the LLM to limit verbosity and prioritize relevant details. This technique, known as prompt engineering with constraints, is the most effective way to control output length and content without altering model parameters or relying on examples that may not generalize.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Set temperature to 0.
Why it's wrong here
Low temperature reduces randomness but does not enforce brevity.
Increase max_tokens to 500.
Why it's wrong here
More tokens encourage longer outputs, not shorter.
Add constraints like 'Max 30 words. Focus on key features.'
Why this is correct
Explicit constraints directly limit length and scope.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Include a few examples of desired short descriptions.
Why it's wrong here
Examples help but without explicit constraints, model may still be verbose.
Common exam traps
Oracle often tests the misconception that adjusting model parameters (temperature or max_tokens) is the primary way to control output quality, when in fact prompt engineering with explicit constraints is a more direct and reliable method for achieving specific formatting or length requirements.
Trap categories for this question
Command / output trap
More tokens encourage longer outputs, not shorter.
Detailed technical explanation
Under the hood, LLMs generate tokens sequentially based on probability distributions; constraints like word limits act as a hard stop on the generation process, forcing the model to prioritize key tokens early. In practice, combining constraints with a low temperature (e.g., 0.2) can further reduce verbosity by minimizing the chance of sampling less probable, irrelevant tokens. This approach is commonly used in production systems where output length must be strictly controlled, such as in e-commerce product feeds.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
The correct answer is: Add constraints like 'Max 30 words. Focus on key features.' — Option C is correct because adding explicit constraints like 'Max 30 words. Focus on key features.' directly instructs the LLM to limit verbosity and prioritize relevant details. This technique, known as prompt engineering with constraints, is the most effective way to control output length and content without altering model parameters or relying on examples that may not generalize.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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