- A
Embedding model quality and relevance to domain
A model not trained on similar data may produce poor embeddings.
- B
Chunk size and overlap strategy
Improper chunking can cause important information to be missed or poorly represented.
- C
Quality of the query embedding generation
If the query is not embedded correctly, the search will not align with semantic intent.
- D
The number of results returned (k) in the search
Why wrong: While k affects recall, the question is about underlying factors causing low recall, not tuning parameters.
- E
The LLM's temperature setting
Why wrong: Temperature affects generation randomness, not retrieval recall.
Question 579 of 991
1Z0-1127 Practice Question: Building LLM Applications with RAG and Vector Search
This 1Z0-1127 practice question tests your understanding of building llm applications with rag and vector search. 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 developer is troubleshooting low recall in a vector search. Which THREE factors should be checked? (Choose three.)
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
✓
Embedding model quality and relevance to domain
Option A is correct because the embedding model's quality and domain relevance directly determine how well semantic relationships are captured. If the model is not fine-tuned on domain-specific data, it may fail to map similar concepts close together in the vector space, leading to low recall. For example, a general-purpose model may not distinguish between 'bank' as a financial institution versus a river bank in a legal document search.
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.
- ✓
Embedding model quality and relevance to domain
Why this is correct
A model not trained on similar data may produce poor embeddings.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Chunk size and overlap strategy
Why this is correct
Improper chunking can cause important information to be missed or poorly represented.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Quality of the query embedding generation
Why this is correct
If the query is not embedded correctly, the search will not align with semantic intent.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The number of results returned (k) in the search
Why it's wrong here
While k affects recall, the question is about underlying factors causing low recall, not tuning parameters.
- ✗
The LLM's temperature setting
Why it's wrong here
Temperature affects generation randomness, not retrieval recall.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that retrieval parameters like k or generation parameters like temperature affect recall, when in fact recall is primarily determined by embedding quality, chunking strategy, and query embedding fidelity.
Detailed technical explanation
How to think about this question
Under the hood, recall in vector search depends on the cosine similarity or dot product distances between query and document embeddings. If chunk size is too large, each chunk may contain multiple topics, diluting the semantic signal; if too small, context is lost. Overlap strategy ensures boundary continuity, preventing information loss at chunk edges. In real-world RAG pipelines, tuning chunk size (e.g., 256–512 tokens) and overlap (e.g., 10–20%) is critical for balancing recall and computational cost.
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 1Z0-1127 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.
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Building LLM Applications with RAG and Vector Search — study guide chapter
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
Building LLM Applications with RAG and Vector Search — This question tests Building LLM Applications with RAG and Vector Search — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Embedding model quality and relevance to domain — Option A is correct because the embedding model's quality and domain relevance directly determine how well semantic relationships are captured. If the model is not fine-tuned on domain-specific data, it may fail to map similar concepts close together in the vector space, leading to low recall. For example, a general-purpose model may not distinguish between 'bank' as a financial institution versus a river bank in a legal document search.
What should I do if I get this 1Z0-1127 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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