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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	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 3	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 4	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 5	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.

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Oracle AI Vector Search Professional Sample Questions (Q56-Q61):

NEW QUESTION # 56
Which function is used to generate vector embeddings within an Oracle database?

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

Answer: A

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 # 57
You are tasked with creating a table to store vector embeddings with the following characteristics: Each vector must have exactly 512 dimensions, and the dimensions should be stored as 32-bitfloating point numbers. Which SQL statement should you use?

A. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512))
B. CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8))
C. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32))
D. CREATE TABLE vectors (id NUMBER, embedding VECTOR)

Answer: C

Explanation:
In Oracle 23ai, the VECTOR data type can specify dimensions and precision. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32)) (D) defines a column with exactly 512 dimensions and FLOAT32 (32-bit float) format, meeting both requirements. Option A omits the format (defaults vary), risking mismatch. Option B is unspecified, allowing variable dimensions-not "exactly 512." Option C uses INT8, not FLOAT32, and '*' denotes undefined dimensions. Oracle's SQL reference confirms this syntax for precise VECTOR definitions.

NEW QUESTION # 58
Which Python library is used to vectorize text chunks and the user's question in the following example?
import oracledb
connection = oracledb.connect(user=un, password=pw, dsn=ds)
table_name = "Page"
with connection.cursor() as cursor:
create_table_sql = f"""
CREATE TABLE IF NOT EXISTS {table_name} (
id NUMBER PRIMARY KEY,
payload CLOB CHECK (payload IS JSON),
vector VECTOR
)"""
try:
cursor.execute(create_table_sql)
except oracledb.DatabaseError as e:
raise
connection.autocommit = True
from sentence_transformers import SentenceTransformer
encoder = SentenceTransformer('all-MiniLM-L12-v2')

A. oracledb
B. sentence_transformers
C. oci
D. json

Answer: B

Explanation:
In the provided Python code, the sentence_transformers library (A) is imported and used to instantiate a SentenceTransformer object with the 'all-MiniLM-L12-v2' model. This library is designed to vectorize text (e.g., chunks and questions) into embeddings, a common step in RAG applications. The oracledb library (C) handles database connectivity, not vectorization. oci (B) is for OCI service interaction, not text embedding. json (D) processes JSON data, not vectors. The code explicitly uses sentence_transformers for vectorization, consistent with Oracle's examples for external embedding integration.

NEW QUESTION # 59
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. Dropping an existing VECTOR column from the table
C. Modifying the data type of an existing VECTOR column to a non-VECTOR type
D. Creating a new table using CTAS (CREATE TABLE AS SELECT) that includes the VECTOR column from the original table

Answer: C

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 # 60
What is the primary function of AI Smart Scan in Exadata System Software 24ai?

A. To provide real-time monitoring and diagnostics for AI applications
B. To accelerate AI workloads by leveraging Exadata RDMA Memory (XRMEM), Exadata Smart Cache, and on-storage processing
C. To automatically optimize database queries for improved performance

Answer: B

Explanation:
AI Smart Scan in Exadata System Software 24ai (B) accelerates AI workloads, including vector search, by offloading processing to storage servers using Exadata's RDMA Memory (XRMEM), Smart Cache, and on-storage capabilities. This enhances performance for large-scale vector operations. Real-time monitoring (A) isn't its focus; that's for management tools. Queryoptimization (C) is a general Exadata feature (Smart Scan), but AI Smart Scan specifically targets AI tasks. Oracle's 24ai documentation emphasizes its role in speeding up AI computations.

NEW QUESTION # 61
......

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