- A
Provide the full contract context in a single prompt.
Why wrong: Contract length often exceeds context window, leading to truncation.
- B
Set top-p=0.1 to limit the vocabulary to the most likely tokens.
Why wrong: Low top-p restricts vocabulary but does not preserve clause meaning.
- C
Fine-tune the model on a parallel corpus of legal translations.
Fine-tuning on domain-specific translations improves accuracy.
- D
Use a glossary of key legal terms with their translations.
A glossary ensures consistent and correct translation of specialized terms.
- E
Increase the temperature to allow more creative phrasing.
Why wrong: Higher temperature increases randomness, risking alteration of meaning.
Question 828 of 997
Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
This Generative AI Leader practice question tests your understanding of techniques to improve generative ai model output. 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 is using a large language model for automated translation of legal contracts. They find that the translations sometimes alter the meaning of specific clauses. Which TWO approaches would most effectively preserve the original meaning? (Choose two.)
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
✓
Fine-tune the model on a parallel corpus of legal translations.
Option C is correct because fine-tuning on a parallel corpus of legal translations adapts the model's weights to the specific domain, ensuring that legal terminology and clause structures are translated with high fidelity. This supervised learning approach directly aligns the model's output with ground-truth translations, preserving original meaning by learning the precise mapping between source and target legal texts.
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.
- ✗
Provide the full contract context in a single prompt.
Why it's wrong here
Contract length often exceeds context window, leading to truncation.
- ✗
Set top-p=0.1 to limit the vocabulary to the most likely tokens.
Why it's wrong here
Low top-p restricts vocabulary but does not preserve clause meaning.
- ✓
Fine-tune the model on a parallel corpus of legal translations.
Why this is correct
Fine-tuning on domain-specific translations improves accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use a glossary of key legal terms with their translations.
Why this is correct
A glossary ensures consistent and correct translation of specialized terms.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the temperature to allow more creative phrasing.
Why it's wrong here
Higher temperature increases randomness, risking alteration of meaning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that prompt engineering alone (Option A) or parameter tweaks like top-p and temperature (Options B and E) can substitute for domain-specific fine-tuning or explicit glossary control, when in fact these methods do not address the root cause of semantic drift in specialized translations.
Detailed technical explanation
How to think about this question
Fine-tuning leverages transfer learning by updating all model parameters on a curated parallel corpus (e.g., aligned legal clauses in English and French), which adjusts the probability distribution over tokens for legal contexts. In contrast, prompt engineering (Option A) relies on in-context learning, which is less reliable for specialized domains because the model's base weights are not optimized for legal jargon. A real-world scenario: a contract clause like 'force majeure' may be mistranslated as 'superior force' without fine-tuning, whereas a fine-tuned model learns the standard legal equivalent (e.g., 'cas fortuit' in French).
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Techniques to Improve Generative AI Model Output — This question tests Techniques to Improve Generative AI Model Output — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Fine-tune the model on a parallel corpus of legal translations. — Option C is correct because fine-tuning on a parallel corpus of legal translations adapts the model's weights to the specific domain, ensuring that legal terminology and clause structures are translated with high fidelity. This supervised learning approach directly aligns the model's output with ground-truth translations, preserving original meaning by learning the precise mapping between source and target legal texts.
What should I do if I get this Generative AI Leader 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
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