- A
Reduce the chunk size of documents
Why wrong: Chunk size can affect context but is not the most direct lever for relevance.
- B
Switch from an HNSW index to a flat index
Why wrong: Index type affects speed, not necessarily relevance.
- C
Increase the top-k retrieval count
Why wrong: More results may include less relevant ones, worsening precision.
- D
Change the similarity metric from cosine to dot product and use a different embedding model
The similarity metric and embedding quality are primary drivers of retrieval relevance.
Question 304 of 1,000
AI0-001 AI Infrastructure and Technologies Practice Question
This AI0-001 practice question tests your understanding of ai infrastructure 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 uses a vector database to store embeddings for a RAG application. Users report that some queries return irrelevant results. Which adjustment is most likely to improve relevance?
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
✓
Change the similarity metric from cosine to dot product and use a different embedding model
Switching from cosine similarity to dot product and using a different embedding model can improve relevance because the choice of similarity metric must align with the embedding model's training objective. Many modern embedding models (e.g., text-embedding-ada-002) are optimized for dot product or cosine similarity, but if the current model was trained for cosine and the queries are not normalized, dot product may better capture magnitude and direction. A different model may also produce higher-quality embeddings that better represent semantic relationships, directly addressing 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.
- ✗
Reduce the chunk size of documents
Why it's wrong here
Chunk size can affect context but is not the most direct lever for relevance.
- ✗
Switch from an HNSW index to a flat index
Why it's wrong here
Index type affects speed, not necessarily relevance.
- ✗
Increase the top-k retrieval count
Why it's wrong here
More results may include less relevant ones, worsening precision.
- ✓
Change the similarity metric from cosine to dot product and use a different embedding model
Why this is correct
The similarity metric and embedding quality are primary drivers of retrieval relevance.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that changing the index type or retrieval count directly improves relevance, when the root cause is usually a mismatch between the similarity metric and the embedding model's training objective.
Detailed technical explanation
How to think about this question
Cosine similarity measures the angle between vectors and is invariant to magnitude, while dot product considers both angle and magnitude. If the embedding model produces vectors where magnitude carries semantic information (e.g., confidence or specificity), dot product can yield more relevant matches. The HNSW index uses a multi-layer graph structure for approximate nearest neighbor search; its recall depends on the similarity metric and embedding quality, not on the index type alone. Real-world RAG systems often fine-tune the embedding model and similarity metric together to align with the domain-specific corpus.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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 AI0-001 question test?
AI Infrastructure and Technologies — This question tests AI Infrastructure and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change the similarity metric from cosine to dot product and use a different embedding model — Switching from cosine similarity to dot product and using a different embedding model can improve relevance because the choice of similarity metric must align with the embedding model's training objective. Many modern embedding models (e.g., text-embedding-ada-002) are optimized for dot product or cosine similarity, but if the current model was trained for cosine and the queries are not normalized, dot product may better capture magnitude and direction. A different model may also produce higher-quality embeddings that better represent semantic relationships, directly addressing irrelevant results.
What should I do if I get this AI0-001 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 AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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