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1Z0-184-25日本語学習内容、1Z0-184-25認定資格

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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

• Building a RAG Application: This section assesses the knowledge of AI Solutions Architects in implementing retrieval-augmented generation (RAG) applications. Candidates will learn to build RAG applications using PL
• SQL and Python to integrate AI models with retrieval techniques for enhanced AI-driven decision-making.

トピック 2

• 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.

トピック 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.

トピック 4

• 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.

トピック 5

• 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.

 

>> 1Z0-184-25日本語学習内容 <<

1Z0-184-25認定資格、1Z0-184-25教育資料

当社の1Z0-184-25試験シミュレーションは、多くの専門家によって選ばれ、質問と回答を常に補完および調整します。 1Z0-184-25学習教材を使用すると、いつでも必要な情報を見つけることができます。 1Z0-184-25準備の質問を更新するとき、社会の変化を考慮し、ユーザーのフィードバックも引き出します。 1Z0-184-25学習教材の使用に関してご意見やご意見がありましたら、お知らせください。私たちはあなたとともに成長したいと思っています。1Z0-184-25トレーニングエンジンの継続的な改善は、最高品質の体験を提供することです。

Oracle AI Vector Search Professional 認定 1Z0-184-25 試験問題 (Q46-Q51):

質問 # 46
A database administrator wants to change the VECTOR_MEMORY_SIZE parameter for a pluggable database (PDB) in Oracle Database 23ai. Which SQL command is correct?

• A. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH
• B. ALTER SYSTEM RESET VECTOR_MEMORY_SIZE
• C. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=SGA
• D. ALTER DATABASE SET VECTOR_MEMORY_SIZE=1G SCOPE=VECTOR

正解：A

解説：
VECTOR_MEMORY_SIZE in Oracle 23ai controls memory allocation for vector operations (e.g., indexing, search) in the SGA. For a PDB, ALTER SYSTEM adjusts parameters, andSCOPE=BOTH (A) applies the change immediately and persists it across restarts (modifying the SPFILE). Syntax: ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH sets it to 1 GB. Option B (ALTER DATABASE) is invalid for this parameter, and SCOPE=VECTOR isn't a valid scope. Option C (SCOPE=SGA) isn't a scope value; valid scopes are MEMORY, SPFILE, or BOTH. Option D (RESET) reverts to default, not sets a value. In a PDB, this must be executed in the PDB context, not CDB, and BOTH ensures durability-key for production environments where vector workloads demand consistent memory.

 

質問 # 47
Which SQL statement correctly adds a VECTOR column named "v" with 4 dimensions and FLOAT32 format to an existing table named "my_table"?

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

正解：C

解説：
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.

 

質問 # 48
When generating vector embeddings outside the database, what is the most suitable option for storing the embeddings for later use?

• A. In a binary FVEC file with the relational data in a CSV file
• B. In a dedicated vector database
• C. In the database as BLOB (Binary Large Object) data
• D. In a CSV file

正解：B

解説：
When vector embeddings are generated outside the database, the storage choice must balance efficiency, scalability, and usability for similarity search. A CSV file (A) is simple and human-readable but inefficient for large-scale vector operations due to text parsing overhead and lack of indexing support. A binary FVEC file (B) offers a compact format for vectors, reducing storage size and improving read performance, but separating relational data into a CSV complicates integration and querying, making it suboptimal for unified workflows. Storing embeddings as BLOBs in a relational database (C) integrates well with structured data and supports SQL access, but it lacks the specialized indexing (e.g., HNSW, IVF) and query optimizations that dedicated vector databases provide. A dedicated vector database (D), such as Milvus or Pinecone (or Oracle 23ai's vector capabilities if internal), is purpose-built for high-dimensional vectors, offering efficient storage, advanced indexing, and fast approximate nearest neighbor (ANN) searches. For external generation scenarios, where embeddings are not immediately integrated into Oracle 23ai, a dedicated vector database is the most suitable due to its performance and scalability advantages. Oracle's AI Vector Search documentation indirectly supports this by emphasizing optimized vector storage for search efficiency, though it focuses on in-database solutions.

 

質問 # 49
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?

• A. 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
• B. Rely on SQL*Loader's automatic normalization of vector data
• C. Enclose vector components in curly braces ({})
• D. Use sparse format for vector data

正解：C

解説：
SQLLoader in Oracle 23ai supports loading VECTOR data from CSV files, requiring vectors to be formatted as text. A critical consideration is enclosing components in curly braces (A), e.g., {1.2, 3.4, 5.6}, to match the VECTOR type's expected syntax (parsed into FLOAT32, etc.). FVEC (B) is a binary format, not compatible with CSV text input; SQLLoader expects readable text, not fixed offsets. Sparse format (C) isn't supported for VECTOR columns, which require dense arrays. SQLLoader doesn't normalize vectors automatically (D); formatting must be explicit. Oracle's documentation specifies curly braces for CSV-loaded vectors.

 

質問 # 50
In Oracle Database 23ai, which SQL function calculates the distance between two vectors using the Euclidean metric?

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

正解：C

解説：
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.

 

質問 # 51
......

JPTestKingの専門家チームがOracleの1Z0-184-25認証試験に対して最新の短期有効なトレーニングプログラムを研究しました。Oracleの1Z0-184-25「Oracle AI Vector Search Professional」認証試験に参加者に対して30時間ぐらいの短期の育成訓練でらくらくに勉強しているうちに多くの知識を身につけられます。

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