- A
The context window of the LLM is too small
Why wrong: Context window affects how much retrieved text can be used, not retrieval relevance.
- B
The embedding model used does not capture the semantic meaning of the documents effectively
Poor embeddings lead to poor similarity matching.
- C
The chunk size of the documents is too large
Why wrong: While chunk size can affect retrieval, the primary issue is often embedding quality.
- D
The temperature setting in the LLM is too high
Why wrong: Temperature affects generation, not retrieval.
Question 951 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. 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 organization is building a RAG system using Vertex AI Vector Search. They notice that the retrieved documents are not relevant to the user's query. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
The embedding model used does not capture the semantic meaning of the documents effectively
The most likely cause is that the embedding model fails to map the semantic meaning of the documents and queries into a shared vector space effectively. In Vertex AI Vector Search, retrieval quality depends entirely on the cosine similarity between query and document embeddings; if the embeddings are poor, even a perfect vector index will return irrelevant results.
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.
- ✗
The context window of the LLM is too small
Why it's wrong here
Context window affects how much retrieved text can be used, not retrieval relevance.
- ✓
The embedding model used does not capture the semantic meaning of the documents effectively
Why this is correct
Poor embeddings lead to poor similarity matching.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The chunk size of the documents is too large
Why it's wrong here
While chunk size can affect retrieval, the primary issue is often embedding quality.
- ✗
The temperature setting in the LLM is too high
Why it's wrong here
Temperature affects generation, not retrieval.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between retrieval-stage failures (embedding quality) and generation-stage parameters (temperature, context window), leading candidates to incorrectly blame LLM settings for poor retrieval results.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Vector Search uses ScaNN (Scalable Nearest Neighbors) for approximate nearest neighbor search, which relies on dense vector representations. If the embedding model (e.g., textembedding-gecko) is not fine-tuned on domain-specific data or uses an inappropriate dimensionality, the dot-product similarity scores become noisy, causing the index to return documents that are syntactically similar but semantically unrelated. In practice, this often occurs when using a generic embedding model for highly specialized domains like legal or medical text.
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: The embedding model used does not capture the semantic meaning of the documents effectively — The most likely cause is that the embedding model fails to map the semantic meaning of the documents and queries into a shared vector space effectively. In Vertex AI Vector Search, retrieval quality depends entirely on the cosine similarity between query and document embeddings; if the embeddings are poor, even a perfect vector index will return irrelevant results.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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