- A
Switch from Vertex AI Search to a different vector database like Pinecone.
Why wrong: Switching to a different vector database like Pinecone does not address the core issue of low-relevance retrieval; the quality of similarity search depends on the embedding and threshold, not the database vendor.
- B
Reduce the model temperature to 0.0 to make outputs more deterministic.
Why wrong: Reducing temperature to 0.0 increases determinism but does not fix hallucinations caused by irrelevant or incorrect context; the model may still confidently generate wrong answers based on poor retrieval.
- C
Increase the similarity score threshold for retrieval to 0.85 to filter out less relevant chunks.
Increasing the similarity threshold to 0.85 filters out low-relevance chunks (e.g., order statuses from other customers), ensuring the model receives only highly relevant context, which directly reduces hallucinated order statuses in this RAG pipeline.
- D
Increase the top-K retrieval value to 10 to provide more context to the model.
Why wrong: Increasing top-K to 10 adds more chunks, but if those additional chunks have low similarity, they introduce irrelevant or conflicting information, potentially worsening hallucinations instead of improving accuracy.
Generative AI Leader Fundamentals of Generative AI Practice Question
This Generative AI Leader practice question tests your understanding of fundamentals of generative ai. 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.
A retail company is building a generative AI chatbot to assist customers with product recommendations and order tracking. The chatbot uses Vertex AI with Gemini 1.5 Pro, and the development team has implemented a Retrieval-Augmented Generation (RAG) pipeline using Vertex AI Search for grounding. The pipeline uses a vector store containing product descriptions and order history. During testing, the team observes that the chatbot sometimes provides incorrect order statuses—for example, claiming an order is 'shipped' when it is actually 'pending'. The team suspects the issue is related to how context is retrieved and used. The RAG pipeline currently retrieves the top 5 chunks based on cosine similarity from the vector store, and passes them as context to the model. The team is considering several changes to improve factual accuracy. Which single action would most effectively reduce hallucinations in this scenario?
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
Increase the similarity score threshold for retrieval to 0.85 to filter out less relevant chunks.
Option C is correct because increasing the similarity score threshold to 0.85 ensures that only highly relevant chunks are passed to the Gemini 1.5 Pro model, directly reducing the risk of the model generating responses based on irrelevant or low-confidence context. In a RAG pipeline using Vertex AI Search, low-similarity chunks can contain order statuses from different customers or products, leading to hallucinations like incorrect order statuses. Filtering out these less relevant chunks improves the factual grounding of the model's output.
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 from Vertex AI Search to a different vector database like Pinecone.
Why it's wrong here
Switching to a different vector database like Pinecone does not address the core issue of low-relevance retrieval; the quality of similarity search depends on the embedding and threshold, not the database vendor.
- ✗
Reduce the model temperature to 0.0 to make outputs more deterministic.
Why it's wrong here
Reducing temperature to 0.0 increases determinism but does not fix hallucinations caused by irrelevant or incorrect context; the model may still confidently generate wrong answers based on poor retrieval.
- ✓
Increase the similarity score threshold for retrieval to 0.85 to filter out less relevant chunks.
Why this is correct
Increasing the similarity threshold to 0.85 filters out low-relevance chunks (e.g., order statuses from other customers), ensuring the model receives only highly relevant context, which directly reduces hallucinated order statuses in this RAG pipeline.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the top-K retrieval value to 10 to provide more context to the model.
Why it's wrong here
Increasing top-K to 10 adds more chunks, but if those additional chunks have low similarity, they introduce irrelevant or conflicting information, potentially worsening hallucinations instead of improving accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that simply adding more context (higher top-K) or making the model more deterministic (lower temperature) will fix hallucinations, when the real issue is the relevance and quality of the retrieved context in a RAG pipeline.
Trap categories for this question
Similar concept trap
Switching to a different vector database like Pinecone does not address the core issue of low-relevance retrieval; the quality of similarity search depends on the embedding and threshold, not the database vendor.
Detailed technical explanation
How to think about this question
In a RAG pipeline, the similarity score threshold acts as a precision filter: chunks with cosine similarity below the threshold are discarded, ensuring only semantically close matches are used as context. Vertex AI Search uses embeddings generated by models like text-embedding-004, and a threshold of 0.85 is a common starting point for high-stakes retrieval tasks. In practice, tuning this threshold requires balancing recall and precision; for order status queries, precision is critical to avoid mixing up statuses across different orders.
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?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the similarity score threshold for retrieval to 0.85 to filter out less relevant chunks. — Option C is correct because increasing the similarity score threshold to 0.85 ensures that only highly relevant chunks are passed to the Gemini 1.5 Pro model, directly reducing the risk of the model generating responses based on irrelevant or low-confidence context. In a RAG pipeline using Vertex AI Search, low-similarity chunks can contain order statuses from different customers or products, leading to hallucinations like incorrect order statuses. Filtering out these less relevant chunks improves the factual grounding of the model's output.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 30, 2026
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