- A
The embedding model dimension mismatch
Why wrong: Dimension mismatch typically causes an error, not empty results.
- B
The k-NN algorithm is misconfigured (e.g., k=0)
Misconfiguration like k=0 causes no candidates to be returned.
- C
The query embedding is out of distribution
Why wrong: Out-of-distribution queries may still return some results, though poor.
- D
The database connection string is incorrect
A wrong connection string prevents any query from reaching the index.
- E
The index is not fully built or refreshed
If the index is not built/refreshed, searches return no results.
Question 419 of 991
Fix No Results: k=0, Bad Connection, Unrefreshed Index
This 1Z0-1127 practice question tests your understanding of building llm applications with rag and vector search. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
Which THREE of the following are likely causes if retrieval returns no results despite documents being indexed in an OCI OpenSearch vector store?
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 k-NN algorithm is misconfigured (e.g., k=0)
Option B is correct because setting k=0 in a k-NN search means requesting zero nearest neighbors, which will always return an empty result set regardless of indexed documents. OCI OpenSearch uses the k-NN plugin where k must be a positive integer (typically ≥1) to retrieve any results; a misconfiguration like k=0 is a common operational error that causes no matches to be returned.
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 embedding model dimension mismatch
Why it's wrong here
Dimension mismatch typically causes an error, not empty results.
- ✓
The k-NN algorithm is misconfigured (e.g., k=0)
Why this is correct
Misconfiguration like k=0 causes no candidates to be returned.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The query embedding is out of distribution
Why it's wrong here
Out-of-distribution queries may still return some results, though poor.
- ✓
The database connection string is incorrect
Why this is correct
A wrong connection string prevents any query from reaching the index.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
The index is not fully built or refreshed
Why this is correct
If the index is not built/refreshed, searches return no results.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
In OCI, the distinction between configuration errors that cause silent failures (like k=0) versus errors that raise exceptions (like dimension mismatch) is important. Candidates may confuse 'out of distribution' embeddings with a retrieval failure when in fact they still return results.
Detailed technical explanation
How to think about this question
OCI OpenSearch's k-NN plugin uses approximate nearest neighbor (ANN) algorithms like HNSW or IVF; the k parameter defines the number of neighbors to return, and setting k=0 is invalid because the algorithm cannot compute zero neighbors. The index refresh behavior is controlled by the `refresh_interval` setting (default 1 second) or explicit `_refresh` API calls; without a refresh, newly indexed vectors are not visible to search queries, which is a common pitfall in real-time ingestion pipelines.
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 network engineer at a university connects two campus buildings via a fibre link. Both routers run OSPF, but no adjacency forms — even though both routers can ping each other. The engineer finds one router is in area 0 and the other in area 1. OSPF adjacency requires matching area numbers, hello/dead timers, and network type. IP reachability alone is not enough.
What to study next
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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 k-NN algorithm is misconfigured (e.g., k=0) — Option B is correct because setting k=0 in a k-NN search means requesting zero nearest neighbors, which will always return an empty result set regardless of indexed documents. OCI OpenSearch uses the k-NN plugin where k must be a positive integer (typically ≥1) to retrieve any results; a misconfiguration like k=0 is a common operational error that causes no matches to be returned.
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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