- A
Using a larger model like Gemini Ultra
Why wrong: A larger model does not inherently ground its answers in external data; it may still hallucinate.
- B
Fine‑tuning on the private knowledge base
Why wrong: Fine‑tuning memorizes knowledge but does not provide dynamic grounding; it can also lead to overfitting.
- C
Increasing the temperature to 0.9
Why wrong: Higher temperature increases randomness and may increase hallucinations.
- D
Retrieval-Augmented Generation (RAG)
RAG injects retrieved knowledge into the prompt, grounding the response in the source.
- E
Prompt engineering to instruct the model to answer based only on the provided context
Explicit instructions can help the model limit its answers to the given context, reducing hallucinations.
Question 784 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 is evaluating how to ground a generative AI model to reduce hallucinations when answering questions about a private knowledge base. Which TWO techniques are most suitable?
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
✓
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is the most suitable technique because it retrieves relevant, up-to-date chunks from the private knowledge base at inference time and conditions the generative model's output on that retrieved context. This grounds the model in factual data, directly reducing hallucinations by ensuring answers are based on the retrieved evidence rather than the model's parametric memory alone.
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.
- ✗
Using a larger model like Gemini Ultra
Why it's wrong here
A larger model does not inherently ground its answers in external data; it may still hallucinate.
- ✗
Fine‑tuning on the private knowledge base
Why it's wrong here
Fine‑tuning memorizes knowledge but does not provide dynamic grounding; it can also lead to overfitting.
- ✗
Increasing the temperature to 0.9
Why it's wrong here
Higher temperature increases randomness and may increase hallucinations.
- ✓
Retrieval-Augmented Generation (RAG)
Why this is correct
RAG injects retrieved knowledge into the prompt, grounding the response in the source.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Prompt engineering to instruct the model to answer based only on the provided context
Why this is correct
Explicit instructions can help the model limit its answers to the given context, reducing hallucinations.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
In the context of the Google Generative AI Leader exam, fine-tuning is often mistakenly thought to be the primary method for grounding a model on private data, when in fact RAG is preferred for dynamic or large knowledge bases because it avoids retraining and allows real-time updates without modifying model weights.
Detailed technical explanation
How to think about this question
RAG works by embedding the private knowledge base into a vector database (e.g., using FAISS or Pinecone), then at query time performing a similarity search (e.g., cosine similarity on embeddings from models like text-embedding-ada-002) to retrieve the top-k relevant passages. These passages are concatenated into the prompt as context, and the generative model (e.g., GPT-4) is instructed to answer solely from that context, effectively decoupling knowledge storage from generation. A subtle behavior is that the retrieval quality directly impacts hallucination reduction—poor chunking strategies or low relevance thresholds can still introduce irrelevant or contradictory information, requiring careful tuning of chunk size and overlap.
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?
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: Retrieval-Augmented Generation (RAG) — Retrieval-Augmented Generation (RAG) is the most suitable technique because it retrieves relevant, up-to-date chunks from the private knowledge base at inference time and conditions the generative model's output on that retrieved context. This grounds the model in factual data, directly reducing hallucinations by ensuring answers are based on the retrieved evidence rather than the model's parametric memory alone.
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
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