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Oracle 1Z0-184-25인증시험인기덤프문제 & 1Z0-184-25최고품질덤프샘플문제

Itcertkr는 많은 분들이Oracle인증1Z0-184-25시험을 응시하여 성공하도록 도와주는 사이트입니다Itcertkr의 Oracle인증1Z0-184-25 학습가이드는 시험의 예상문제로 만들어진 아주 퍼펙트한 시험자료입니다. Oracle인증1Z0-184-25시험은 최근 가장 인기있는 시험으로 IT인사들의 사랑을 독차지하고 있으며 국제적으로 인정해주는 시험이라 어느 나라에서 근무하나 제한이 없습니다. Itcertkr로 여러분은 소유하고 싶은 인증서를 빠른 시일내에 얻게 될것입니다.

Oracle 1Z0-184-25 시험요강:

주제
소개

주제 1

Using Vector Indexes: This section evaluates the expertise of AI Database Specialists in optimizing vector searches using indexing techniques. It covers the creation of vector indexes to enhance search speed, including the use of HNSW and IVF vector indexes for performing efficient search queries in AI-driven applications.

주제 2

Leveraging Related AI Capabilities: This section evaluates the skills of Cloud AI Engineers in utilizing Oracle’s AI-enhanced capabilities. It covers the use of Exadata AI Storage for faster vector search, Select AI with Autonomous for querying data using natural language, and data loading techniques using SQL Loader and Oracle Data Pump to streamline AI-driven workflows.

주제 3

Using Vector Embeddings: This section measures the abilities of AI Developers in generating and storing vector embeddings for AI applications. It covers generating embeddings both inside and outside the Oracle database and effectively storing them within the database for efficient retrieval and processing.

>> Oracle 1Z0-184-25인증시험 인기 덤프문제 <<

1Z0-184-25인증시험 인기 덤프문제 완벽한 시험 최신버전 덤프

Oracle인증 1Z0-184-25시험을 어떻게 패스할가 고민그만하고Itcertkr의Oracle 인증1Z0-184-25시험대비 덤프를 데려가 주세요.가격이 착한데 비해 너무나 훌륭한 덤프품질과 높은 적중율, Itcertkr가 아닌 다른곳에서 찾아볼수 없는 혜택입니다.

최신 Oracle Database 23ai 1Z0-184-25 무료샘플문제 (Q24-Q29):

질문 # 24
Which SQL statement correctly adds a VECTOR column named "v" with 4 dimensions and FLOAT32 format to an existing table named "my_table"?

A. ALTER TABLE my_table ADD v VECTOR(4, FLOAT32)
B. ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32))
C. ALTER TABLE my_table MODIFY (v VECTOR(4, FLOAT32))
D. UPDATE my_table SET v = VECTOR(4, FLOAT32)

정답：B

설명：
To add a new column to an existing table, Oracle uses the ALTER TABLE statement with the ADD clause. Option B, ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32)), correctly specifies the column name "v", the VECTOR type, and its attributes (4 dimensions, FLOAT32 precision) within parentheses, aligning with Oracle's DDL syntax for VECTOR columns. Option A uses MODIFY, which alters existing columns, not adds new ones, making it incorrect here. Option C uses UPDATE, a DML statement for updating data, not a DDL operation for schema changes. Option D omits parentheses around the VECTOR specification, which is syntactically invalid as Oracle requires dimensions and format to be enclosed. The SQL Language Reference confirms this syntax for adding VECTOR columns.

질문 # 25
What is the primary function of an embedding model in the context of vector search?

A. To transform text or data into numerical vector representations
B. To define the schema for a vector database
C. To store vectors in a structured format for efficient retrieval
D. To execute similarity search operations within a database

정답：A

설명：
An embedding model in the context of vector search, such as those used in Oracle Database 23ai, is fundamentally a machine learning construct (e.g., BERT, SentenceTransformer, or an ONNX model) designed to transform raw data-typically text, but also images or other modalities-into numerical vector representations (C). These vectors, stored in the VECTOR data type, encapsulate semantic meaning in a high-dimensional space where proximity reflects similarity. For instance, the word "cat" might be mapped to a 512-dimensional vector like [0.12, -0.34, ...], where its position relative to "dog" indicates relatedness. This transformation is the linchpin of vector search, enabling mathematical operations like cosine distance to find similar items.
Option A (defining schema) misattributes a database design role to the model; schema is set by DDL (e.g., CREATE TABLE with VECTOR). Option B (executing searches) confuses the model with database functions like VECTOR_DISTANCE, which use the embeddings, not create them. Option D (storing vectors) pertains to the database's storage engine, not the model's function-storage is handled by Oracle's VECTOR type and indexes (e.g., HNSW). The embedding model's role is purely generative, not operational or structural. In practice, Oracle 23ai integrates this via VECTOR_EMBEDDING, which calls the model to produce vectors, underscoring its transformative purpose. Misunderstanding this could lead to conflating data preparation with query execution, a common pitfall for beginners.

질문 # 26
In Oracle Database 23ai, which SQL function calculates the distance between two vectors using the Euclidean metric?

A. HAMMING_DISTANCE
B. L1_DISTANCE
C. L2_DISTANCE
D. COSINE_DISTANCE

정답：C

설명：
In Oracle Database 23ai, vector distance calculations are primarily handled by the VECTOR_DISTANCE function, which supports multiple metrics (e.g., COSINE, EUCLIDEAN) specified as parameters (e.g., VECTOR_DISTANCE(v1, v2, EUCLIDEAN)). However, the question implies distinct functions, a common convention in some databases or libraries, and Oracle's documentation aligns L2_DISTANCE (B) with the Euclidean metric. L2 (Euclidean) distance is the straight-line distance between two points in vector space, computed as √∑(xi - yi)², where xi and yi are vector components. For example, for vectors [1, 2] and [4, 6], L2 distance is √((1-4)² + (2-6)²) = √(9 + 16) = 5.
Option A, L1_DISTANCE, represents Manhattan distance (∑|xi - yi|), summing absolute differences-not Euclidean. Option C, HAMMING_DISTANCE, counts differing bits, suited for binary vectors (e.g., INT8), not continuous Euclidean spaces typically used with FLOAT32 embeddings. Option D, COSINE_DISTANCE (1 - cosine similarity), measures angular separation, distinct from Euclidean's magnitude-inclusive approach. While VECTOR_DISTANCE is the general function in 23ai, L2_DISTANCE may be an alias or a contextual shorthand in some Oracle AI examples, reflecting Euclidean's prominence in geometric similarity tasks. Misinterpreting this could lead to choosing COSINE for spatial tasks where magnitude matters, skewing results. Oracle's vector search framework supports Euclidean via VECTOR_DISTANCE, but B aligns with the question's phrasing.

질문 # 27
Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

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

정답：C

설명：
RAG in Oracle Database 23ai combines vector search with LLMs to enhance responses by retrieving relevant private data from the database (e.g., via VECTOR columns) and augmenting LLM prompts. This (A) improves context-awareness and precision, leveraging enterprise-specific data without retraining LLMs. Optimizing LLM performance (B) is a secondary benefit, not the core focus. Training specialized LLMs (C) is not RAG's purpose; it uses existing models. Real-time streaming (D) is possible but not the primary benefit, as RAG focuses on stored data retrieval. Oracle's RAG documentation emphasizes private data integration for better LLM outputs.

질문 # 28
Which DDL operation is NOT permitted on a table containing a VECTOR column in Oracle Database 23ai?

A. Dropping an existing VECTOR column from the table
B. Creating a new table using CTAS (CREATE TABLE AS SELECT) that includes the VECTOR column from the original table
C. Modifying the data type of an existing VECTOR column to a non-VECTOR type
D. Adding a new VECTOR column to the table

정답：C

설명：
Oracle Database 23ai imposes restrictions on DDL operations for tables with VECTOR columns to preserve data integrity. CTAS (A) is permitted, as it copies the VECTOR column intact into a new table, maintaining its structure. Dropping a VECTOR column (B) is allowed via ALTER TABLE DROP COLUMN, as it simply removes the column without altering its type. Adding a new VECTOR column (D) is supported with ALTER TABLE ADD, enabling schema evolution. However, modifying an existing VECTOR column's data type to a non-VECTOR type (C) (e.g., VARCHAR2, NUMBER) is not permitted because VECTOR is a specialized type with dimensional and format constraints (e.g., FLOAT32), and Oracle does not support direct type conversion due to potential loss of semantic meaning and structure. This restriction is documented in Oracle's SQL reference.

질문 # 29
......

많은 시간과 돈이 필요 없습니다. 30분이란 특별학습가이드로 여러분은Oracle 1Z0-184-25인증시험을 한번에 통과할 수 있습니다, Itcertkr에서Oracle 1Z0-184-25시험자료의 문제와 답이 실제시험의 문제와 답과 아주 비슷한 덤프만 제공합니다.

1Z0-184-25최고품질 덤프샘플문제: https://www.itcertkr.com/1Z0-184-25_exam.html

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