- A
The search algorithm is set to exact (brute-force) and index is small
Why wrong: Exact search still finds matches if they exist.
- B
The query embedding dimension does not match the index dimension
Dimension mismatch causes search errors or zero results.
- C
The query is too long for the embedding model
Why wrong: Models typically truncate long inputs, but still produce an embedding.
- D
The index has not been refreshed after adding new documents
Why wrong: New documents are immediately searchable unless refresh interval is disabled.
- E
The embedding model used for indexing is different from the one used for query
Different models produce incompatible embeddings, so no results match.
Question 456 of 991
Why Vector Search Returns No Results Due to Dimension Mismatch
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 troubleshooting scenario: A RAG system returns no results for certain queries. The index exists and has documents. Which TWO are likely causes?
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 query embedding dimension does not match the index dimension
Option B is correct because vector search requires the query embedding to have the same dimensionality as the index vectors. If the query embedding dimension (e.g., 768 from a different model) does not match the index dimension (e.g., 1024), the similarity computation fails or returns no results. This is a fundamental requirement in all vector databases like FAISS, Pinecone, or pgvector.
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 search algorithm is set to exact (brute-force) and index is small
Why it's wrong here
Exact search still finds matches if they exist.
- ✓
The query embedding dimension does not match the index dimension
Why this is correct
Dimension mismatch causes search errors or zero results.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The query is too long for the embedding model
Why it's wrong here
Models typically truncate long inputs, but still produce an embedding.
- ✗
The index has not been refreshed after adding new documents
Why it's wrong here
New documents are immediately searchable unless refresh interval is disabled.
- ✓
The embedding model used for indexing is different from the one used for query
Why this is correct
Different models produce incompatible embeddings, so no results match.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the subtle distinction between 'index not refreshed' (which only affects new documents) and 'embedding model mismatch' (which breaks all queries), leading candidates to incorrectly select option D.
Detailed technical explanation
How to think about this question
Under the hood, vector databases store embeddings as fixed-length arrays. When querying, the system computes a distance metric (e.g., cosine similarity or L2) between the query vector and each indexed vector; a dimension mismatch raises a runtime error or silently returns empty results. In production, this often occurs when teams switch embedding models (e.g., from sentence-transformers/all-MiniLM-L6-v2 with 384 dimensions to text-embedding-ada-002 with 1536 dimensions) without rebuilding the index.
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: The query embedding dimension does not match the index dimension — Option B is correct because vector search requires the query embedding to have the same dimensionality as the index vectors. If the query embedding dimension (e.g., 768 from a different model) does not match the index dimension (e.g., 1024), the similarity computation fails or returns no results. This is a fundamental requirement in all vector databases like FAISS, Pinecone, or pgvector.
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
This 1Z0-1127 practice question is part of Courseiva's free Oracle 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 1Z0-1127 exam.
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