- A
Deploy the model with a larger max_output_tokens
Why wrong: Max tokens does not affect hallucination or consistency.
- B
Use prompt engineering with few-shot examples
Why wrong: Few-shot may help but not as effective as fine-tuning for consistency.
- C
Increase the temperature to 0.9
Why wrong: Higher temperature increases randomness, making outputs less consistent.
- D
Perform supervised fine-tuning using their labeled dataset
Fine-tuning adapts the model to the specific summarization style and reduces errors.
Question 295 of 997
Align Summarization Models with Supervised Fine-Tuning
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. 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 organization is deploying a summarization model on Vertex AI and needs to ensure that the model's responses are consistent and avoid hallucinations. They have a labeled dataset of source documents and human-written summaries. Which approach would best align the model with their quality requirements?
Quick Answer
The answer is supervised fine-tuning using their labeled dataset. This approach directly addresses the need for reducing hallucinations via supervised fine-tuning because it trains the model on ground-truth examples of source documents paired with human-written summaries, teaching it to map inputs to accurate, concise outputs rather than generating fabricated details. On the Google Cloud Generative AI Leader exam, this question tests your understanding of alignment techniques—specifically that supervised fine-tuning on task-specific, high-quality data improves factual consistency, whereas methods like prompt engineering or RLHF without curated labels are less reliable for eliminating hallucinations. A common trap is assuming that a larger base model alone will reduce errors, but without fine-tuning on domain-specific summaries, the model may still invent content. Memory tip: think “labeled pairs lock in precision”—the dataset is the key to aligning the model’s behavior with your quality requirements.
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
✓
Perform supervised fine-tuning using their labeled dataset
Supervised fine-tuning (D) directly optimizes the model's weights using the labeled dataset of source documents and human-written summaries, which teaches the model to produce consistent, factual outputs and reduces hallucinations. This approach aligns the model's behavior with the specific quality requirements by learning from ground-truth examples, unlike prompt engineering or parameter adjustments that do not modify the underlying model.
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.
- ✗
Deploy the model with a larger max_output_tokens
Why it's wrong here
Max tokens does not affect hallucination or consistency.
- ✗
Use prompt engineering with few-shot examples
Why it's wrong here
Few-shot may help but not as effective as fine-tuning for consistency.
- ✗
Increase the temperature to 0.9
Why it's wrong here
Higher temperature increases randomness, making outputs less consistent.
- ✓
Perform supervised fine-tuning using their labeled dataset
Why this is correct
Fine-tuning adapts the model to the specific summarization style and reduces errors.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often overestimate the power of prompt engineering or parameter tweaks, mistakenly believing they can achieve the same level of reliability as fine-tuning, when in fact only supervised fine-tuning directly modifies the model to internalize the labeled data's quality standards.
Trap categories for this question
Command / output trap
Higher temperature increases randomness, making outputs less consistent.
Detailed technical explanation
How to think about this question
Supervised fine-tuning on Vertex AI uses techniques like LoRA or full fine-tuning to update the model's parameters based on the labeled dataset, effectively teaching the model domain-specific patterns and factual associations. This process reduces the likelihood of hallucination by reinforcing the mapping from source documents to verified summaries, as the model learns to prioritize learned patterns over generative creativity. In practice, fine-tuning also allows for lower temperature settings (e.g., 0.1–0.3) during inference, further enhancing consistency.
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?
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Perform supervised fine-tuning using their labeled dataset — Supervised fine-tuning (D) directly optimizes the model's weights using the labeled dataset of source documents and human-written summaries, which teaches the model to produce consistent, factual outputs and reduces hallucinations. This approach aligns the model's behavior with the specific quality requirements by learning from ground-truth examples, unlike prompt engineering or parameter adjustments that do not modify the underlying model.
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
This Generative AI Leader practice question is part of Courseiva's free Google Cloud 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 Generative AI Leader exam.
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