Building LLM Applications with RAG and Vector Search →hardMultiple ChoiceObjective-mapped

1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Exhibit

Refer to the exhibit.

document index mapping:
{
  "settings": {
    "index": {
      "knn": true,
      "knn.space_type": "cosinesimil"
    }
  },
  "mappings": {
    "properties": {
      "content_embedding": {
        "type": "knn_vector",
        "dimension": 768,
        "method": {
          "name": "hnsw",
          "engine": "faiss",
          "space_type": "l2"
        }
      },
      "metadata": {
        "type": "object"
      }
    }
  }
}

An engineer configured the above index mapping for vector search. When performing a k-NN search, the results are unexpected. What is the most likely issue?

⚠ Common exam trap

Oracle often tests the nuance that OpenSearch requires consistency between index-level and mapping-level space_type settings, a detail that candidates overlook because they assume only the mapping-level setting matters.

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 space type at the index level and mapping level are mismatched.

OpenSearch requires the space type to be consistently defined at both the index-level settings (method.parameters.space_type) and the field-level mapping (space_type). A mismatch between these two causes the k-NN search to behave unexpectedly, as the engine uses the index-level setting for distance computation while the mapping-level setting may be used for validation or other purposes.

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 space type 'cosinesimil' is not supported; it should be 'cosine'.
Why it's wrong here
'cosinesimil' is valid.
✗
The dimension 768 does not match the embedding model's output dimension.
Why it's wrong here
768 is a common dimension.
✗
The mapping uses 'knn_vector' type with 'faiss' engine, which is incompatible.
Why it's wrong here
faiss is a supported engine.
✓
The space type at the index level and mapping level are mismatched.
Why this is correct
Mismatch causes incorrect distance calculations.

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1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Exhibit

Why each option matters

Option-by-option breakdown

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