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. 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.
Exhibit
Refer to the exhibit.
```sql
-- Oracle Database 23ai AI Vector Search index creation
CREATE VECTOR INDEX doc_vec_idx ON documents(chunk_embedding)
ORGANIZATION NEIGHBOR PARTITIONS
DISTANCE COSINE
WITH TARGET ACCURACY 95
PARAMETERS (TYPE IVF, NEIGHBOR PARTITIONS 4);
```
A DBA has created the above vector index. After running queries, they observe that recall is lower than expected for approximate searches. Which change would most likely improve recall while maintaining query performance?
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.
Exhibit
Refer to the exhibit.
```sql
-- Oracle Database 23ai AI Vector Search index creation
CREATE VECTOR INDEX doc_vec_idx ON documents(chunk_embedding)
ORGANIZATION NEIGHBOR PARTITIONS
DISTANCE COSINE
WITH TARGET ACCURACY 95
PARAMETERS (TYPE IVF, NEIGHBOR PARTITIONS 4);
```
A
Change the index type from IVF to HNSW.
Why wrong: While HNSW generally offers better recall, the question asks for a change within the current index context; TARGET ACCURACY is more direct.
B
Increase the TARGET ACCURACY value to 99.
A higher TARGET ACCURACY forces the approximate search to consider more vectors, increasing recall at the cost of some latency.
C
Increase the number of neighbor partitions (NEIGHBOR PARTITIONS) to 8.
Why wrong: More partitions can improve parallelism but may not directly improve recall; recall is controlled by TARGET ACCURACY.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the TARGET ACCURACY value to 99.
Increasing TARGET ACCURACY to 99 directly raises the recall threshold for approximate search, forcing the vector index to retrieve more candidates during the search phase. This improves recall without changing the index structure or query parallelism, so query performance (latency) is only minimally impacted compared to switching index types or drastically altering neighbor partitions.
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.
✗
Change the index type from IVF to HNSW.
Why it's wrong here
While HNSW generally offers better recall, the question asks for a change within the current index context; TARGET ACCURACY is more direct.
✓
Increase the TARGET ACCURACY value to 99.
Why this is correct
A higher TARGET ACCURACY forces the approximate search to consider more vectors, increasing recall at the cost of some latency.
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.
✗
Increase the number of neighbor partitions (NEIGHBOR PARTITIONS) to 8.
Why it's wrong here
More partitions can improve parallelism but may not directly improve recall; recall is controlled by TARGET ACCURACY.
✗
Reduce the number of neighbor partitions to 2.
Why it's wrong here
Fewer partitions reduce the search space, likely lowering recall.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that changing the index type (e.g., IVF to HNSW) is the primary way to fix recall, when in fact TARGET ACCURACY is the direct parameter for recall tuning without altering the index structure.
Detailed technical explanation
How to think about this question
TARGET ACCURACY in Oracle Vector Search controls the minimum recall percentage the approximate search must achieve; setting it to 99 means the search will dynamically adjust the number of probes (e.g., via a higher nprobes parameter) until the recall target is met. Under the hood, this uses a feedback loop during query execution to balance between scanning more centroids (IVF) or more layers (HNSW) to hit the recall threshold, making it a fine-grained tuning knob. In real-world RAG pipelines, this parameter is critical when embedding quality varies, as it ensures consistent retrieval quality without requiring index rebuilds.
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
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.
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: Increase the TARGET ACCURACY value to 99. — Increasing TARGET ACCURACY to 99 directly raises the recall threshold for approximate search, forcing the vector index to retrieve more candidates during the search phase. This improves recall without changing the index structure or query parallelism, so query performance (latency) is only minimally impacted compared to switching index types or drastically altering neighbor partitions.
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.
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.
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Question Discussion
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