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LangChain and AI Application DevelopmenthardMultiple ChoiceObjective-mapped

1Z0-1127-25 LangChain and AI Application Development Practice Question

An organization needs to implement a RAG application with Oracle AI Vector Search but has strict latency requirements. They have millions of vectors. Which index type is likely to provide the best search speed while maintaining reasonable recall?

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

IVF (Inverted File) index

IVF (Inverted File) partitions the vector space into clusters, reducing search scope. It typically offers faster search than exhaustive search and good recall, especially for large datasets. HNSW may also be fast but can have higher memory usage. BTREE is for scalar data. Exhaustive search is too slow.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • No index, relying on the VECTOR data type only

    Why it's wrong here

    Without an index, similarity search is exhaustive and will be slow for large datasets.

  • IVF (Inverted File) index

    Why this is correct

    IVF uses clustering to limit search to a subset of vectors, offering a good trade-off between speed and recall.

  • BTREE index

    Why it's wrong here

    BTREE is designed for scalar values, not for vector similarity search.

  • Exhaustive search (no index)

    Why it's wrong here

    Exhaustive search compares the query against all vectors, which is too slow for millions of vectors.

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