- A
Set top_p to 0.1
Why wrong: Reduces diversity, not factuality.
- B
Reduce output length
Why wrong: Does not address factual correctness.
- C
Grounded generation with citations
Anchors answers to external evidence.
- D
Increase model size
Why wrong: May reduce hallucinations but not as effectively as grounding.
Question 143 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.
To improve factuality in generative AI, which is the best approach?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
✓
Grounded generation with citations
Grounded generation with citations directly addresses factuality by forcing the model to retrieve and cite verifiable sources (e.g., from a knowledge base or document store) before generating an answer. This approach, often implemented via retrieval-augmented generation (RAG), ensures outputs are anchored to external evidence rather than relying solely on the model's parametric memory, which can produce hallucinations. Citations also enable users to verify claims, making this the most effective technique for improving factual accuracy.
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.
- ✗
Set top_p to 0.1
Why it's wrong here
Reduces diversity, not factuality.
- ✗
Reduce output length
Why it's wrong here
Does not address factual correctness.
- ✓
Grounded generation with citations
Why this is correct
Anchors answers to external evidence.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase model size
Why it's wrong here
May reduce hallucinations but not as effectively as grounding.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google exams often test the misconception that hyperparameter tuning (like top_p) or scaling model size alone can fix factuality, when in reality these methods do not address the root cause of hallucination—lack of external grounding—and candidates may overlook the importance of retrieval and citation mechanisms, such as Google's Vertex AI Grounding or RAG.
Detailed technical explanation
How to think about this question
Grounded generation typically uses a retriever (e.g., dense passage retrieval with embeddings) to fetch relevant documents from a vector database, then passes those documents as context to the generative model via a prompt template. The model is instructed to produce answers only from the provided context, often with inline citations like [1] or [Source: page X]. A subtle behavior is that the model may still hallucinate if the retrieved context is irrelevant or if the prompt does not enforce strict adherence—so techniques like instruction tuning or constrained decoding (e.g., forcing citation tokens) are used to improve reliability. In real-world deployments, such as enterprise chatbots for legal or medical advice, grounded generation with citations is critical to meet compliance and auditability requirements.
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
Got this wrong? Here's your next step.
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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: Grounded generation with citations — Grounded generation with citations directly addresses factuality by forcing the model to retrieve and cite verifiable sources (e.g., from a knowledge base or document store) before generating an answer. This approach, often implemented via retrieval-augmented generation (RAG), ensures outputs are anchored to external evidence rather than relying solely on the model's parametric memory, which can produce hallucinations. Citations also enable users to verify claims, making this the most effective technique for improving factual accuracy.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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