- A
Reduce the top-k value to 10
Why wrong: Reducing top-k makes the model more deterministic but does not add factual knowledge.
- B
Implement Retrieval-Augmented Generation (RAG) with a curated knowledge base
RAG provides relevant context from trusted sources, reducing hallucinations.
- C
Use prompt engineering to instruct the model to 'be more accurate'
Why wrong: Prompt instructions alone cannot add external knowledge; the model may still hallucinate.
- D
Increase the temperature to 1.0 for more diverse outputs
Why wrong: Higher temperature increases randomness, likely worsening factual accuracy.
Question 491 of 997
Generative AI Leader Generative AI Concepts and Technologies Practice Question
This Generative AI Leader practice question tests your understanding of generative ai concepts and technologies. 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 has a Gemini-based application that sometimes produces factually incorrect answers. They want to improve accuracy without retraining the model. Which technique should they implement?
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 Retrieval-Augmented Generation (RAG) with a curated knowledge base
Retrieval-Augmented Generation (RAG) is the correct technique because it grounds the model's responses in a curated, external knowledge base, providing factual context that reduces hallucinations without modifying the model's weights. This directly addresses the need for improved accuracy in a Gemini-based application while avoiding costly retraining.
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.
- ✗
Reduce the top-k value to 10
Why it's wrong here
Reducing top-k makes the model more deterministic but does not add factual knowledge.
- ✓
Implement Retrieval-Augmented Generation (RAG) with a curated knowledge base
Why this is correct
RAG provides relevant context from trusted sources, reducing hallucinations.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use prompt engineering to instruct the model to 'be more accurate'
Why it's wrong here
Prompt instructions alone cannot add external knowledge; the model may still hallucinate.
- ✗
Increase the temperature to 1.0 for more diverse outputs
Why it's wrong here
Higher temperature increases randomness, likely worsening factual accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that prompt engineering alone can fix factual accuracy issues, when in reality, without external knowledge grounding, the model remains reliant on its parametric memory which is prone to hallucination.
Detailed technical explanation
How to think about this question
RAG works by first retrieving relevant documents from a vector database using embedding-based similarity search (e.g., cosine similarity on embeddings from a model like text-embedding-004), then concatenating the retrieved context with the user query before feeding it to the Gemini model for generation. This process effectively constrains the model's output to the retrieved factual data, reducing hallucinations. In practice, the curated knowledge base must be regularly updated and indexed with appropriate chunking strategies to ensure high retrieval precision.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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?
Generative AI Concepts and Technologies — This question tests Generative AI Concepts and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement Retrieval-Augmented Generation (RAG) with a curated knowledge base — Retrieval-Augmented Generation (RAG) is the correct technique because it grounds the model's responses in a curated, external knowledge base, providing factual context that reduces hallucinations without modifying the model's weights. This directly addresses the need for improved accuracy in a Gemini-based application while avoiding costly retraining.
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
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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