1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A research institution uses OCI Data Flow to process large-scale document corpora for a RAG system. They want to minimize latency for end-user queries. Which architecture decision would most effectively reduce query latency?
⚠ Common exam trap
A common mix-up: candidates confuse batch processing with real-time processing, assuming that more parallelism (Option C) or a better model (Option B) can solve latency issues, when in fact the fundamental latency reduction comes from moving the expensive embedding computation out of the query path 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
✓
Precompute embeddings offline using OCI Data Flow and store them in an OCI OpenSearch index.
Precomputing embeddings offline with OCI Data Flow and storing them in an OCI OpenSearch index eliminates the need to generate embeddings at query time, which is the primary source of latency. This approach shifts the computationally expensive embedding generation to a batch process, allowing queries to perform only a fast vector similarity search against the precomputed index, drastically reducing end-user response time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Embed documents on-the-fly during query time to ensure freshness.
Why it's wrong here
Adds latency.
- ✗
Use a larger, more accurate embedding model.
Why it's wrong here
Larger models increase compute time.
- ✗
Increase the number of Spark workers for parallel processing of queries.
Why it's wrong here
Query processing is typically not done in Spark; it's for batch.
- ✓
Precompute embeddings offline using OCI Data Flow and store them in an OCI OpenSearch index.
Why this is correct
Precomputation removes runtime embedding cost.
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