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100% Pass Quiz 2026 Oracle Fantastic 1Z0-184-25: New Oracle AI Vector Search Professional Exam Name

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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic
Details

Topic 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.

Topic 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.

Topic 3

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.

Topic 4

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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Oracle AI Vector Search Professional Sample Questions (Q46-Q51):

NEW QUESTION # 46
What is the function of the COSINE parameter in the SQL query used to retrieve similar vectors?
topk = 3
sql = f"""select payload, vector_distance(vector, :vector, COSINE) as score from {table_name} order by score fetch approximate {topk} rows only"""

A. It converts the vectors to a format compatible with the SQL database
B. It filters out vectors with a cosine similarity below a certain threshold
C. It specifies the type of vector encoding used in the database
D. It indicates that the cosine distance metric should be used to measure similarity between vectors

Answer: D

Explanation:
In Oracle Database 23ai, the VECTOR_DISTANCE function calculates the distance between two vectors using a specified metric. The COSINE parameter in the query (vector_distance(vector, :vector, COSINE)) instructs the database to use the cosine distance metric (C) to measure similarity. Cosine distance, defined as 1 - cosine similarity, is ideal for high-dimensional vectors (e.g., text embeddings) as it focuses on angular separation rather than magnitude. It doesn't filter vectors (A); filtering requires additional conditions (e.g., WHERE clause). It doesn't convert vector formats (B); vectors are already in the VECTOR type. It also doesn't specify encoding (D), which is defined during vector creation (e.g., FLOAT32). Oracle's documentation confirms COSINE as one of the supported metrics for similarity search.

NEW QUESTION # 47
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

Answer: B

Explanation:
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.

NEW QUESTION # 48
What is the primary purpose of a similarity search in Oracle Database 23ai?

A. To group vectors by their exact scores
B. To find exact matches in BLOB data
C. To retrieve the most semantically similar entries using distance metrics between different vectors
D. Optimize relational database operations to compute distances between all data points in a database

Answer: C

Explanation:
Similarity search in Oracle 23ai (C) uses vector embeddings in VECTOR columns to retrieve entries semantically similar to a query vector, based on distance metrics (e.g., cosine, Euclidean) via functions like VECTOR_DISTANCE. This is key for AI applications like RAG, finding "close" rather than exact matches. Optimizing relational operations (A) is unrelated; similarity search is vector-specific. Exact matches in BLOBs (B) don't leverage vector semantics. Grouping by scores (D) is a post-processing step, not the primary purpose. Oracle's documentation defines similarity search as retrieving semantically proximate vectors.

NEW QUESTION # 49
Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?

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

Answer: C

Explanation:
Oracle Data Pump in 23ai natively supports the VECTOR data type (C), allowing export and import of tables with vector columns without conversion or plug-ins. This facilitates vector search application migrations, preserving dimensional and format integrity (e.g., FLOAT32). BLOB storage (A) isn't required; VECTOR is a distinct type. Data Pump doesn't treat vectors as text (B), avoiding corruption; it handles them as structured arrays. No specialized plug-in (D) is needed; native support is built-in. Oracle's Data Pump documentation confirms seamless handling of VECTOR data.

NEW QUESTION # 50
If a query vector uses a different distance metric than the one used to create the index, whathappens?

A. The query fails
B. An exact match search is triggered
C. A warning is logged, but the query executes
D. The index automatically updates

Answer: A

Explanation:
In Oracle Database 23ai, vector indexes (e.g., HNSW, IVF) are built with a specific distance metric (e.g., cosine, Euclidean) that defines how similarity is computed. If a query specifies a different metric (e.g., querying with Euclidean on a cosine-based index), the index cannot be used effectively, and the query fails (A) with an error, as the mismatch invalidates the index's structure. An exact match search (B) doesn't occur automatically; Oracle requires explicit control. The index doesn't update itself (C), and warnings (D) are not the default behavior-errors are raised instead. Oracle's documentation mandates metric consistency for index usage.

NEW QUESTION # 51
......

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