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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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.

Topic 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.

Topic 4

Understand Vector Fundamentals: This section of the exam measures the skills of Data Engineers in working with vector data types for storing embeddings and enabling semantic queries. It covers vector distance functions and metrics used in AI vector search. Candidates must demonstrate proficiency in performing DML and DDL operations on vectors to manage data efficiently.

Topic 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.

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Oracle AI Vector Search Professional Sample Questions (Q18-Q23):

NEW QUESTION # 18
You are asked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?

A. Apply relational filters and a similarity search in the query
B. Use UNION ALL with vector operations
C. Use VECTOR_INDEX_HINT and NO WHERE clause
D. Perform the similarity search without a WHERE clause

Answer: A

Explanation:
To fetch the top five nearest vectors for a specific category, combine relational filtering (e.g., WHERE category = 'X') with similarity search (C) (e.g., VECTOR_DISTANCE with ORDER BY and FETCH FIRST 5 ROWS). UNION ALL (A) is for combining result sets, not filtering. Omitting WHERE (B) ignores the category constraint. VECTOR_INDEX_HINT (D) influences index usage, not filtering, and skipping WHERE misses the requirement. Oracle's vector search examples use WHERE clauses with similarity functions for such tasks.

NEW QUESTION # 19
What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?

A. HNSW is partition-based, whereas IVF uses neighbor graphs for indexing
B. HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions
C. HNSW guarantees accuracy, whereas IVF sacrifices performance for accuracy
D. Both operate identically but differ in memory usage

Answer: B

NEW QUESTION # 20
Which function is used to generate vector embeddings within an Oracle database?

A. DBMS_VECTOR_CHAIN.UTL_TO_TEXT
B. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS
C. DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS
D. DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT

Answer: C

Explanation:
In Oracle 23ai, the DBMS_VECTOR_CHAIN package provides utilities for vector workflows. UTL_TO_EMBEDDINGS (C) generates vector embeddings from text within the database, typically using an ONNX model, supporting RAG and search applications. UTL_TO_CHUNKS (A) splits text, not generates embeddings. UTL_TO_TEXT (B) converts documents to text, a preprocessing step. UTL_TO_GENERATE_TEXT (D) doesn't exist; text generation is handled by LLMs, not this package. Oracle's documentation identifies UTL_TO_EMBEDDINGS as the embedding creation function in PL/SQL workflows.

NEW QUESTION # 21
Which PL/SQL function converts documents such as PDF, DOC, JSON, XML, or HTML to plain text?

A. DBMS_VECTOR.TEXT_TO_PLAIN
B. DBMS_VECTOR.CONVERT_TO_TEXT
C. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS
D. DBMS_VECTOR_CHAIN.UTL_TO_TEXT

Answer: D

Explanation:
In Oracle Database 23ai, DBMS_VECTOR_CHAIN.UTL_TO_TEXT is the PL/SQL function that converts documents in formats like PDF, DOC, JSON, XML, or HTML into plain text, a key step in preparing data for vectorization in RAG workflows. DBMS_VECTOR.TEXT_TO_PLAIN (A) is not a valid function. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS (C) splits text into smaller segments, not converts documents. DBMS_VECTOR.CONVERT_TO_TEXT (D) does not exist in the documented packages. UTL_TO_TEXT is part of the DBMS_VECTOR_CHAIN package, designed for vector processing pipelines, and is explicitly noted for document conversion in Oracle's documentation.

NEW QUESTION # 22
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 MODIFY (v VECTOR(4, FLOAT32))
B. ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32))
C. UPDATE my_table SET v = VECTOR(4, FLOAT32)
D. ALTER TABLE my_table ADD v VECTOR(4, FLOAT32)

Answer: B

Explanation:
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.

NEW QUESTION # 23
......

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