1Z0-1127-25 LangChain and AI Application Development Practice Question
An organization is deploying a RAG application with Oracle AI Vector Search. They need to ensure that the vector index supports low-latency queries and can handle updates to the underlying documents (inserts, deletes, modifications) without significant performance degradation. Which two index features should they consider? (Choose TWO.)
⚠ Common exam trap
The 1Z0-1127 exam often tests the misconception that HNSW indexes are always superior for dynamic workloads, but the question explicitly asks for two features that support low-latency queries and handle updates, and both IVF with periodic rebuilds and HNSW are valid; the trap is that candidates might overlook the periodic rebuild requirement for IVF or incorrectly assume HNSW is the only option, leading them to select only one correct answer or to dismiss IVF entirely.
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
✓
Use an IVF index with periodic rebuilds to maintain performance after many updates
An IVF (Inverted File) index with periodic rebuilds is well-suited for RAG applications that experience frequent updates (inserts, deletes, modifications). IVF indexes are designed for approximate nearest neighbor search, offering low-latency queries, but they can degrade over time as data changes; periodic rebuilds restore performance without requiring a full re-index of the entire dataset. This approach balances query speed with update tolerance, making it a practical choice for dynamic document collections in Oracle AI Vector Search.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a VECTOR data type with a default B-tree index
Why it's wrong here
B-tree indexes are not suitable for vector similarity search; they are designed for exact matches on scalar data.
- ✓
Use an IVF index with periodic rebuilds to maintain performance after many updates
Why this is correct
IVF indexes can be rebuilt periodically to handle updates; with proper maintenance, they can provide low-latency queries.
- ✗
Enable exact nearest neighbor search to avoid index maintenance
Why it's wrong here
Exact search is slow and does not scale; it is not an index type.
- ✗
Disable indexing and rely on full table scan for simplicity
Why it's wrong here
Full table scans are too slow for low-latency requirements.
- ✓
Use an HNSW index, which supports incremental updates and provides low-latency search
Why this is correct
HNSW indexes are efficient for approximate nearest neighbor search and support dynamic insertion and deletion.
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