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1z0-184-25 Exam Dumps - Oracle Cloud Infrastructure Questions and Answers

Question # 4

Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?

Options:

A.

Data Pump only exports and imports vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database

B.

Data Pump treats vector embeddings as regular text strings, which can lead to data corruption or loss of precision when transferring vector data for vector search

C.

Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications

D.

Because of the complexity of vector data, Data Pump requires a specialized plug-in to handle the export and import operations involving vector data types

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Question # 5

When using SQL*Loader to load vector data for search applications, what is a critical consideration regarding the formatting of the vector data within the input CSV file?

Options:

A.

Enclose vector components in curly braces ({})

B.

As FVEC is a binary format and the vector dimensions have a known width, fixed offsets can be used to make parsing the vectors fast and efficient

C.

Use sparse format for vector data

D.

Rely on SQL*Loader’s automatic normalization of vector data

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Question # 6

What are the key advantages and considerations of using Retrieval Augmented Generation (RAG) in the context of Oracle AI Vector Search?

Options:

A.

It excels at optimizing the performance and efficiency of LLM inference through advanced caching and precomputation techniques, leading to faster response times but potentially increasing storage requirements

B.

It prioritizes real-time data extraction and summarization from various sources to ensure the LLM always has the most up-to-date information

C.

It focuses on training specialized LLMs within the database environment for specific tasks, offering greater control over model behavior and data privacy but potentially requiring more development effort

D.

It leverages existing database security and access controls, thereby enabling secure and controlled access to both the database content and the LLM

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Question # 7

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

Options:

A.

The number of centroids decreases

B.

Accuracy decreases

C.

Index creation time is reduced

D.

More partitions are probed, improving accuracy, but also increasing query latency

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Question # 8

What is the primary function of an embedding model in the context of vector search?

Options:

A.

To define the schema for a vector database

B.

To execute similarity search operations within a database

C.

To transform text or data into numerical vector representations

D.

To store vectors in a structured format for efficient retrieval

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Question # 9

Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

Options:

A.

It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries

B.

It primarily aims to optimize the performance and efficiency of LLMs by using advanced data retrieval techniques, thus minimizing response times and reducing computational overhead

C.

It allows users to train their own specialized LLMs directly within the Oracle Database environment using their internal data, thereby reducing reliance on external AI providers

D.

It enables Large Language Models (LLMs) to access and process real-time data streams from diverse sources to generate the most up-to-date insights

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Question # 10

What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?

Options:

A.

Load ONNX Model, Vectorize Question, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output

B.

Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output

C.

Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output

D.

Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, Perform Vector Search, Generate Output

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Question # 11

You need to generate a vector from the string '[1.2, 3.4]' in FLOAT32 format with 2 dimensions. Which function will you use?

Options:

A.

TO_VECTOR

B.

VECTOR_DISTANCE

C.

FROM_VECTOR

D.

VECTOR_SERIALIZE

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Question # 12

Which function is used to generate vector embeddings within an Oracle database?

Options:

A.

DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS

B.

DBMS_VECTOR_CHAIN.UTL_TO_TEXT

C.

DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS

D.

DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT

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Question # 13

What is the primary purpose of the DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS package in a RAG application?

Options:

A.

To generate vector embeddings from a text document

B.

To load a document into the database

C.

To split a large document into smaller chunks to improve vector quality by minimizing token truncation

D.

To convert a document into a single, large text string

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Exam Code: 1z0-184-25
Exam Name: Oracle AI Vector Search Professional
Last Update: Mar 29, 2025
Questions: 60
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