- A
Fine-tuning the model on the corpus
Fine-tuning adapts the model to the domain, improving answer quality while RAG provides fresh knowledge.
- B
Retrieval-Augmented Generation (RAG)
RAG retrieves relevant passages at query time, grounding responses and reducing hallucinations.
- C
Using a zero-shot prompt
Why wrong: Zero-shot prompting does not incorporate external knowledge and is prone to hallucination.
- D
Using only the largest Gemini model with no retrieval
Why wrong: Even the largest model has a knowledge cutoff and may hallucinate.
- E
Increasing the model's temperature to 1.5
Why wrong: Higher temperature increases randomness, likely increasing hallucinations.
Question 45 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 data scientist wants to build a question-answering system over a large corpus of scientific papers. They want to minimize hallucinations and keep the knowledge current. Which TWO techniques should they combine?
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
✓
Fine-tuning the model on the corpus
Fine-tuning the model on the corpus (A) adapts the model's weights to the specific domain and style of scientific papers, improving relevance and reducing factual errors. Retrieval-Augmented Generation (RAG) (B) grounds each answer in retrieved, up-to-date passages from the corpus, directly countering hallucinations and enabling knowledge updates without retraining. Together, they combine domain adaptation with dynamic retrieval for accurate, current responses.
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.
- ✓
Fine-tuning the model on the corpus
Why this is correct
Fine-tuning adapts the model to the domain, improving answer quality while RAG provides fresh knowledge.
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.
- ✓
Retrieval-Augmented Generation (RAG)
Why this is correct
RAG retrieves relevant passages at query time, grounding responses and reducing hallucinations.
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.
- ✗
Using a zero-shot prompt
Why it's wrong here
Zero-shot prompting does not incorporate external knowledge and is prone to hallucination.
- ✗
Using only the largest Gemini model with no retrieval
Why it's wrong here
Even the largest model has a knowledge cutoff and may hallucinate.
- ✗
Increasing the model's temperature to 1.5
Why it's wrong here
Higher temperature increases randomness, likely increasing hallucinations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that a larger model alone or higher temperature can solve hallucinations, when in fact grounding via retrieval and domain adaptation are the proven mitigations.
Detailed technical explanation
How to think about this question
RAG typically uses a dense retriever (e.g., DPR or ColBERT) to encode corpus passages into a vector index, then retrieves the top-k chunks via cosine similarity for each query. Fine-tuning can be applied to the generator (e.g., T5 or LLaMA) on the corpus to align its language priors with scientific terminology and reasoning patterns. In practice, combining these allows the system to answer questions like 'What is the latest on CRISPR off-target effects?' by retrieving 2024 papers while the fine-tuned model formats the answer with domain-appropriate phrasing.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Fine-tuning the model on the corpus — Fine-tuning the model on the corpus (A) adapts the model's weights to the specific domain and style of scientific papers, improving relevance and reducing factual errors. Retrieval-Augmented Generation (RAG) (B) grounds each answer in retrieved, up-to-date passages from the corpus, directly countering hallucinations and enabling knowledge updates without retraining. Together, they combine domain adaptation with dynamic retrieval for accurate, current responses.
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.
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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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