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2025 1Z0-184-25 Valid Exam Pdf | Professional Oracle 1Z0-184-25: Oracle AI Vector Search Professional 100% Pass

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Oracle AI Vector Search Professional Sample Questions (Q13-Q18):

NEW QUESTION # 13
In Oracle Database 23ai, which data type is used to store vector embeddings for similarity search?

A. VECTOR2
B. VARCHAR2
C. BLOB
D. VECTOR

Answer: D

Explanation:
Oracle Database 23ai introduces the VECTOR data type (C) specifically for storing vector embeddings used in similarity search, supporting dimensions and formats (e.g., FLOAT32, INT8). VECTOR2 (A) doesn't exist. BLOB (B) can store binary data, including vectors, but lacks the semantic structure and indexing support of VECTOR. VARCHAR2 (D) is for text, not numerical arrays. VECTOR is optimized for AI vector search with native indexing (e.g., HNSW, IVF), as per Oracle's documentation.

NEW QUESTION # 14
Why would you choose to NOT define a specific size for the VECTOR column during development?

A. It impacts the accuracy of similarity searches
B. Different external embedding models produce vectors with varying dimensions and data types
C. It limits the length of text that can be vectorized
D. It restricts the database to a single embedding model

Answer: B

Explanation:
In Oracle Database 23ai, a VECTOR column can be defined with a specific size (e.g., VECTOR(512, FLOAT32)) or left unspecified (e.g., VECTOR). Not defining a size (D) provides flexibility during development because different embedding models (e.g., BERT, SentenceTransformer) generate vectors with varying dimensions (e.g., 768, 384) and data types (e.g., FLOAT32, INT8). This avoids locking the schema into one model, allowing experimentation. Accuracy (A) isn't directly impacted by size definition; it depends on the model and metric. A fixed size doesn't restrict the database to one model (B) but requires matching dimensions. Text length (C) affects tokenization, not vector dimensions. Oracle's documentation supports undefined VECTOR columns for flexibility in AI workflows.

NEW QUESTION # 15
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?

A. OCI Data Science
B. OCI Vision
C. OCI Generative AI
D. OCI Language

Answer: C

Explanation:
Select AI in Oracle Database 23ai integrates with OCI Generative AI (B) to process natural language queries and generate context-aware responses using large language models (LLMs). OCI Language (A) focuses on text analysis (e.g., sentiment, entity recognition), not generative tasks. OCI Vision (C) handles image processing, unrelated to Select AI's text-based functionality. OCI Data Science (D) supports model development, not direct integration with Select AI. Oracle's documentation explicitly names OCI Generative AI as the integrated service for Select AI's LLM capabilities.

NEW QUESTION # 16
What happens when querying with an IVF index if you increase the value of the NEIGHBOR_PARTITIONS probes parameter?

A. Accuracy decreases
B. The number of centroids decreases
C. Index creation time is reduced
D. More partitions are probed, improving accuracy, but also increasing query latency

Answer: D

Explanation:
The NEIGHBOR_PARTITIONS parameter in Oracle 23ai's IVF index controls how many partitions are probed during a query. Increasing this value examines more clusters, raising theprobability of finding relevant vectors, thus improving accuracy (recall). However, this increases computational effort, leading to higher query latency-a classic ANN trade-off. The number of centroids (A) is fixed during index creation and unaffected by query parameters. Accuracy does not decrease (B); it improves. Index creation time (C) is unrelated to query-time settings. Oracle's documentation on IVF confirms that NEIGHBOR_PARTITIONS directly governs this accuracy-latency balance.

NEW QUESTION # 17
Which PL/SQL function converts documents such as PDF, DOC, JSON, XML, or HTML to plain text?

A. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS
B. DBMS_VECTOR.CONVERT_TO_TEXT
C. DBMS_VECTOR.TEXT_TO_PLAIN
D. DBMS_VECTOR_CHAIN.UTL_TO_TEXT

Answer: D

Explanation:
In Oracle Database 23ai, DBMS_VECTOR_CHAIN.UTL_TO_TEXT is the PL/SQL function that converts documents in formats like PDF, DOC, JSON, XML, or HTML into plain text, a key step in preparing data for vectorization in RAG workflows. DBMS_VECTOR.TEXT_TO_PLAIN (A) is not a valid function. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS (C) splits text into smaller segments, not converts documents. DBMS_VECTOR.CONVERT_TO_TEXT (D) does not exist in the documented packages. UTL_TO_TEXT is part of the DBMS_VECTOR_CHAIN package, designed for vector processing pipelines, and is explicitly noted for document conversion in Oracle's documentation.

NEW QUESTION # 18
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