- A
Switch to a larger base model (e.g., Gemini Ultra) without any retrieval.
Why wrong: A larger model may still hallucinate on niche topics and increases latency.
- B
Implement a retrieval-augmented generation (RAG) pipeline using Vertex AI Search to fetch relevant documents before generating answers.
RAG dynamically retrieves relevant context, enabling accurate answers for rare issues.
- C
Collect more data on rare issues and continue fine-tuning the model weekly.
Why wrong: Fine-tuning requires large amounts of data and may not scale to every rare issue; also time-consuming.
- D
Use a few-shot prompt with 10 examples of rare problems and solutions.
Why wrong: Few-shot examples are limited and cannot cover the diversity of rare issues.
Question 617 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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 AI team is building a customer support chatbot for a telecom company using a fine-tuned LLM on Vertex AI. The model performs well on common issues but fails to answer correctly for rare or novel problems, often providing plausible-sounding but incorrect solutions. The team has a large corpus of internal troubleshooting documents. They want to minimize incorrect answers while keeping latency low. Which approach should they take?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
Implement a retrieval-augmented generation (RAG) pipeline using Vertex AI Search to fetch relevant documents before generating answers.
Option B is correct because implementing a RAG pipeline with Vertex AI Search allows the chatbot to retrieve relevant troubleshooting documents from the internal corpus in real-time, grounding the LLM's responses in authoritative sources. This approach directly addresses the problem of plausible-sounding but incorrect answers for rare/novel issues without requiring retraining, and it keeps latency low by fetching only the most relevant documents before generation.
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.
- ✗
Switch to a larger base model (e.g., Gemini Ultra) without any retrieval.
Why it's wrong here
A larger model may still hallucinate on niche topics and increases latency.
- ✓
Implement a retrieval-augmented generation (RAG) pipeline using Vertex AI Search to fetch relevant documents before generating answers.
Why this is correct
RAG dynamically retrieves relevant context, enabling accurate answers for rare issues.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Collect more data on rare issues and continue fine-tuning the model weekly.
Why it's wrong here
Fine-tuning requires large amounts of data and may not scale to every rare issue; also time-consuming.
- ✗
Use a few-shot prompt with 10 examples of rare problems and solutions.
Why it's wrong here
Few-shot examples are limited and cannot cover the diversity of rare issues.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that fine-tuning or larger models alone can solve knowledge gaps, when in fact retrieval-augmented generation is the standard approach for grounding LLM outputs in up-to-date, domain-specific documents without retraining.
Detailed technical explanation
How to think about this question
RAG works by embedding the query and document chunks into a vector space (e.g., using Vertex AI's textembedding-gecko), then performing approximate nearest neighbor search to retrieve the top-k chunks (typically k=3-5) before passing them as context to the LLM. A subtle behavior is that the retrieval step must balance precision and recall: too few chunks may miss critical details, while too many can exceed the LLM's context window or introduce noise, causing the model to ignore the retrieved context. In real-world telecom deployments, RAG pipelines often include a reranker (e.g., Vertex AI Ranker) to improve the relevance of retrieved documents, reducing hallucination rates by over 40% compared to fine-tuning alone.
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: Implement a retrieval-augmented generation (RAG) pipeline using Vertex AI Search to fetch relevant documents before generating answers. — Option B is correct because implementing a RAG pipeline with Vertex AI Search allows the chatbot to retrieve relevant troubleshooting documents from the internal corpus in real-time, grounding the LLM's responses in authoritative sources. This approach directly addresses the problem of plausible-sounding but incorrect answers for rare/novel issues without requiring retraining, and it keeps latency low by fetching only the most relevant documents before generation.
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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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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