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1Z0-184-25最新考題 & 1Z0-184-25考試題庫
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Oracle 1Z0-184-25 考試大綱：
| 主題 |
簡介 |
| 主題 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.
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| 主題 2 |
- 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.
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| 主題 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.
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| 主題 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.
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>> 1Z0-184-25最新考題 <<
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最新的 Oracle Database 23ai 1Z0-184-25 免費考試真題 (Q50-Q55):
問題 #50
What is the default distance metric used by the VECTOR_DISTANCE function if none is specified?
- A. Cosine
- B. Manhattan
- C. Euclidean
- D. Hamming
答案：A
解題說明：
The VECTOR_DISTANCE function in Oracle 23ai computes vector distances, and if no metric is specified (e.g., VECTOR_DISTANCE(v1, v2)), it defaults to Cosine (C). Cosine distance (1 - cosine similarity) is widely used for text embeddings due to its focus on angular separation, ignoring magnitude-fitting for normalized vectors from models like BERT. Euclidean (A) measures straight-line distance, not default. Hamming (B) is for binary vectors, rare in 23ai's FLOAT32 context. Manhattan (D) sums absolute differences, less common for embeddings. Oracle's choice of Cosine reflects its AI focus, as documentation confirms, aligning with industry norms for semantic similarity-vital for users assuming defaults in queries.
問題 #51
You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?
- A. The specific distance algorithm employed for vector comparisons
- B. Regularly updating vector embeddings to reflect changes in the source data
- C. Using the same embedding model for both vector creation and similarity search
- D. The physical storage location of the vector data
答案：C
解題說明：
In Oracle Database 23ai, vector search accuracy hinges on the consistency of the embedding model. The VECTOR data type stores embeddings as fixed-dimensional arrays, and similarity searches (e.g., using VECTOR_DISTANCE) assume that all vectors-stored and query-are generated by the same model. This ensures they occupy the same semantic space, making distance calculations meaningful. Regular updates (B) maintain data freshness, but if the model changes, integrity is compromised unless all embeddings are regenerated consistently. The distance algorithm (C) (e.g., cosine, Euclidean) defines how similarity is measured but relies on consistent embeddings; an incorrect model mismatch undermines any algorithm. Physical storage location (D) affects performance, not integrity. Oracle's documentation stresses model consistency as a prerequisite for reliable vector search within its native capabilities.
問題 #52
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. Adding a new VECTOR column to the table
- C. Creating a new table using CTAS (CREATE TABLE AS SELECT) that includes the VECTOR column from the original table
- D. Modifying the data type of an existing VECTOR column to a non-VECTOR type
答案：D
解題說明：
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.
問題 #53
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?
- A. Multivector similarity search with partitioning
- B. Exact similarity search using a full table scan
- C. Approximate similarity search with IVF indexing and target accuracy of 70%
- D. Approximate similarity search with HNSW indexing and target accuracy of 70%
答案：B
解題說明：
To prioritize accuracy over speed, exact similarity search with a full table scan (C) computes distances between the query vector and all stored vectors, guaranteeing 100% recall without approximation trade-offs. HNSW with 70% target accuracy (A) and IVF with 70% (D) are approximate methods, sacrificing accuracy for speed via indexing (e.g., probing fewer neighbors). Multivector search (B) isn't a standard Oracle 23ai term; partitioning aids scale, not accuracy. Exact search, though slower, ensures maximum accuracy, as per Oracle's vector search options.
問題 #54
Which of the following actions will result in an error when using VECTOR_DIMENSION_COUNT() in Oracle Database 23ai?
- A. Using a vector with a data type that is not supported by the function
- B. Providing a vector with a dimensionality that exceeds the specified dimension count
- C. Providing a vector with duplicate values for its components
- D. Calling the function on a vector that has been created with TO_VECTOR()
答案：A
解題說明：
The VECTOR_DIMENSION_COUNT() function in Oracle 23ai returns the number of dimensions in a VECTOR-type value (e.g., 512 for VECTOR(512, FLOAT32)). It's a metadata utility, not a validator of content or structure beyond type compatibility. Option B-using a vector with an unsupported data type-causes an error because the function expects a VECTOR argument; passing, say, a VARCHAR2 or NUMBER instead (e.g., '1,2,3' or 42) triggers an ORA-error (e.g., ORA-00932: inconsistent datatypes). Oracle enforces strict typing for vector functions.
Option A (exceeding specified dimensions) is a red herring; the function reports the actual dimension count of the vector, not the column's defined limit-e.g., VECTOR_DIMENSION_COUNT(TO_VECTOR('[1,2,3]')) returns 3, even if the column is VECTOR(2), as the error occurs at insertion, not here. Option C (duplicate values, like [1,1,2]) is valid; the function counts dimensions (3), ignoring content. Option D (using TO_VECTOR()) is explicitly supported; VECTOR_DIMENSION_COUNT(TO_VECTOR('[1.2, 3.4]')) returns 2 without issue. Misinterpreting this could lead developers to over-constrain data prematurely-B's type mismatch is the clear error case, rooted in Oracle's vector type system.
問題 #55
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1Z0-184-25考試題庫: https://www.vcesoft.com/1Z0-184-25-pdf.html
