実際的-100％合格率の1Z0-184-25認定テキスト試験-試験の準備方法1Z0-184-25試験復習赤本

1Z0-184-25試験に合格することが、最高のキャリアの機会です。関連する証明書の豊富な経験は、企業があなたの選択のために一連の専門的な空席を開くために重要です。状況によってはあなたを助けたり破ったりすることができるこの運命的な試験について、当社はこれらの1Z0-184-25練習資料を説明責任を持って作成しました。他の場所に受け入れられる可能性が高くなり、より高い給料や受け入れが得られることを理解しています。

Oracle 1Z0-184-25 認定試験の出題範囲：

トピック	出題範囲
トピック 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.
トピック 2	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.
トピック 3	Performing Similarity Search: This section tests the skills of Machine Learning Engineers in conducting similarity searches to find relevant data points. It includes performing exact and approximate similarity searches using vector indexes. Candidates will also work with multi-vector similarity search to handle searches across multiple documents for improved retrieval accuracy.

>> 1Z0-184-25認定テキスト <<

1Z0-184-25試験復習赤本、1Z0-184-25日本語資格取得

製品がどれほど優れていても、ユーザーは使用過程でいくつかの難しい問題に遭遇します。 1Z0-184-25の実際の試験資料も例外ではありません。最高の製品体験を楽しむために、ユーザーが使用中のプロセスで問題が見つかった場合は、1Z0-184-25を初めてチェックして、試験問題のパフォーマンス、ユーザーが問題を解決するのに役立つ専門のメンテナンススタッフ。 1Z0-184-25ラーニングリファレンスファイルには、効率の良い製品メンテナンスチームがあり、数分で1Z0-184-25試験の質問を送信できます。

Oracle AI Vector Search Professional 認定 1Z0-184-25 試験問題 (Q26-Q31):

質問 # 26
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 empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries
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

正解：B

解説：
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.

質問 # 27
Which PL/SQL package is primarily used for interacting with Generative AI services in Oracle Database 23ai?

A. DBMS_ML
B. DBMS_AI
C. DBMS_GENAI
D. DBMS_VECTOR_CHAIN

正解：B

解説：
Oracle Database 23ai introduces DBMS_AI as the primary PL/SQL package for interacting with Generative AI services, such as OCI Generative AI, enabling features like natural language query processing (e.g., Select AI) and AI-driven insights. DBMS_ML (B) focuses on machine learning model training and management, not generative AI. DBMS_VECTOR_CHAIN (C) supports vector processing workflows (e.g., document chunking, embedding), but it's not the main interface for generative AI services. DBMS_GENAI (D) is not a recognized package in 23ai documentation. DBMS_AI's role is highlighted in Oracle's AI integration features for 23ai.

質問 # 28
What happens when querying with an IVF index if you increase the value of the NEIGHBOR_PARTITIONS probes parameter?

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

正解：D

解説：
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.

質問 # 29
What is the first step in setting up the practice environment for Select AI?

A. Create a policy to enable access to OCI Generative AI
B. Create a new user account with elevated privileges
C. Optionally create an OCI compartment
D. Drop any compartment that does not use OCI Generative AI

正解：C

解説：
Select AI in Oracle Database 23ai enables natural language queries by integrating with OCI Generative AI services. The first step in setting up the practice environment is to optionally create an OCI compartment (A), which organizes and isolates resources in Oracle Cloud Infrastructure (OCI). This is foundational because subsequent steps-like defining policies or configuring the Autonomous Database-depend on a compartment structure, though an existing compartment can be reused, making it optional. Creating a policy (B) is a subsequent step to grant access to OCIGenerative AI, requiring a compartment first. Dropping compartments (C) is irrelevant and disruptive. Creating a user account (D) is not specified as the initial step in Select AI setup. Oracle's Select AI documentation lists compartment setup as the starting point in OCI configuration.

質問 # 30
Which DDL operation is NOT permitted on a table containing a VECTOR column in Oracle Database 23ai?

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

正解：B

解説：
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.

質問 # 31
......

人生のチャンスを掴むことができる人は殆ど成功している人です。ですから、ぜひPassTestというチャンスを掴んでください。PassTestのOracleの1Z0-184-25試験トレーニング資料はあなたがOracleの1Z0-184-25認定試験に合格することを助けます。この認証を持っていたら、あなたは自分の夢を実現できます。そうすると人生には意義があります。

1Z0-184-25試験復習赤本: https://www.passtest.jp/Oracle/1Z0-184-25-shiken.html