- A
Increase the temperature to 0.8 to allow more creative interpretations.
Why wrong: Higher temperature increases randomness, reducing accuracy.
- B
Provide few-shot examples of correctly labeled sentiment in the prompt.
Few-shot examples guide the model's output format and accuracy.
- C
Use the model's built-in sentiment analysis API instead of prompting.
Why wrong: This is a different tool, not a technique to improve the current model.
- D
Add a system instruction that asks the model to strictly follow JSON output format.
Why wrong: Formatting does not improve sentiment extraction accuracy.
- E
Fine-tune the model on a labeled dataset of customer feedback.
Fine-tuning adapts the model to the specific task.
Question 423 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 team is using a language model for customer feedback analysis. They want to improve the accuracy of sentiment extraction. Which TWO techniques should they apply? (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
✓
Provide few-shot examples of correctly labeled sentiment in the prompt.
Option B is correct because providing few-shot examples of correctly labeled sentiment in the prompt directly guides the model to understand the desired output format and classification boundaries through in-context learning. This technique leverages the model's ability to generalize from a small number of labeled examples without requiring retraining, improving accuracy for specific sentiment extraction tasks.
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.
- ✗
Increase the temperature to 0.8 to allow more creative interpretations.
Why it's wrong here
Higher temperature increases randomness, reducing accuracy.
- ✓
Provide few-shot examples of correctly labeled sentiment in the prompt.
Why this is correct
Few-shot examples guide the model's output format and accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the model's built-in sentiment analysis API instead of prompting.
Why it's wrong here
This is a different tool, not a technique to improve the current model.
- ✗
Add a system instruction that asks the model to strictly follow JSON output format.
Why it's wrong here
Formatting does not improve sentiment extraction accuracy.
- ✓
Fine-tune the model on a labeled dataset of customer feedback.
Why this is correct
Fine-tuning adapts the model to the specific task.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that increasing temperature or enforcing output format directly improves accuracy, but these techniques affect creativity or structure, not the correctness of the underlying classification.
Detailed technical explanation
How to think about this question
Few-shot prompting works by conditioning the model on input-output pairs within the context window, effectively creating a task-specific prior without weight updates. This leverages the transformer's attention mechanism to align the model's internal representations with the labeled examples, which is particularly effective when the target task (e.g., sentiment analysis) has clear patterns but subtle boundary cases. In real-world scenarios, few-shot examples can be dynamically selected from a database to match the input's domain, further boosting accuracy.
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: Provide few-shot examples of correctly labeled sentiment in the prompt. — Option B is correct because providing few-shot examples of correctly labeled sentiment in the prompt directly guides the model to understand the desired output format and classification boundaries through in-context learning. This technique leverages the model's ability to generalize from a small number of labeled examples without requiring retraining, improving accuracy for specific sentiment extraction tasks.
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.
About these practice questions
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Last reviewed: Jul 4, 2026
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