2025 Exam 1Z0-184-25 Book | The Best 100% Free 1Z0-184-25 Passguide

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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.

Oracle AI Vector Search Professional Sample Questions (Q17-Q22):

NEW QUESTION # 17
In the following Python code, what is the significance of prepending the source filename to each text chunk before storing it in the vector database?
bash
CollapseWrapCopy
docs = [{"text": filename + "|" + section, "path": filename} for filename, sections in faqs.items() for section in sections]
# Sample the resulting data
docs[:2]

A. It preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file
B. It improves the accuracy of the LLM by providing additional training data
C. It helps differentiate between chunks from different files but has no impact on vectorization
D. It speeds up the vectorization process by providing a unique identifier for each chunk

Answer: A

Explanation:
Prepending the filename to each text chunk (e.g., filename + "|" + section) in the Python code (A) preserves contextual metadata, linking each chunk-and its resulting vector-to its source file. This aids retrieval in RAG applications by allowing the application to trace back to the original document, enhancing response context (e.g., "from Book1"). While it differentiates chunks (B), its impact goes beyond identification, affecting retrieval usability. It doesn't speed up vectorization (C); embedding models process text regardless of prefixes. It also doesn't train the LLM (D); it's metadata for retrieval, not training data. Oracle's RAG examples emphasize metadata preservation for context-aware responses.

NEW QUESTION # 18
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. sentence_transformers
B. oci
C. oracledb
D. json

Answer: A

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 # 19
Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

A. It enables Large Language Models (LLMs) to access and process real-time data streams from diverse sources to generate the most up-to-date insights
B. It allows users to train their own specialized LLMs directly within the Oracle Database environment using their internal data, thereby reducing reliance on external AI providers
C. It primarily aims to optimize the performance and efficiency of LLMs by using advanced data retrieval techniques, thus minimizing response times and reducing computational overhead
D. It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries

Answer: D

Explanation:
RAG in Oracle Database 23ai combines vector search with LLMs to enhance responses by retrieving relevant private data from the database (e.g., via VECTOR columns) and augmenting LLM prompts. This (A) improves context-awareness and precision, leveraging enterprise-specific data without retraining LLMs. Optimizing LLM performance (B) is a secondary benefit, not the core focus. Training specialized LLMs (C) is not RAG's purpose; it uses existing models. Real-time streaming (D) is possible but not the primary benefit, as RAG focuses on stored data retrieval. Oracle's RAG documentation emphasizes private data integration for better LLM outputs.

NEW QUESTION # 20
What security enhancement is introduced in Exadata System Software 24ai?

A. SNMP security (Security Network Management Protocol)
B. Enhanced encryption algorithm for data at rest
C. Integration with third-party security tools

Answer: B

Explanation:
Exadata System Software 24ai (noted in context beyond 23ai) introduces an enhanced encryption algorithm for data at rest (B), strengthening security for stored data, including vectors. Third-party integration (A) isn't highlighted as a 24ai feature. SNMP security (C) relates to network monitoring, not a primary Exadata enhancement. Oracle's Exadata documentation for 24ai emphasizes advanced encryption as a key security upgrade.

NEW QUESTION # 21
What are the key advantages and considerations of using Retrieval Augmented Generation (RAG) in the context of Oracle AI Vector Search?

A. It focuses on training specialized LLMs within the database environment for specific tasks, offering greater control over model behavior and data privacy but potentially requiring more development effort
B. It excels at optimizing the performance and efficiency of LLM inference through advanced caching and precomputation techniques, leading to faster response times but potentially increasing storage requirements
C. It leverages existing database security and access controls, thereby enabling secure and controlled access to both the database content and the LLM
D. It prioritizes real-time data extraction and summarization from various sources to ensure the LLM always has the most up-to-date information

Answer: C

Explanation:
RAG in Oracle AI Vector Search integrates vector search with LLMs, leveraging database-stored data. A key advantage is its use of existing database security and access controls (D), ensuring that sensitive enterprise data remains secure while being accessible to LLMs, aligning with Oracle's security model (e.g., roles, privileges). Performance optimization (A) occurs but isn't the primary focus; storage increases are minimal compared to security benefits. Real-time extraction (B) is possible but not RAG's core strength, which lies in static data augmentation. Training LLMs (C) is unrelated to RAG, which uses pre-trained models. Oracle emphasizes security integration as a standout RAG feature.

NEW QUESTION # 22
......

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2025 Exam 1Z0-184-25 Book | The Best 100% Free 1Z0-184-25 Passguide

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Oracle 1Z0-184-25 Exam Syllabus Topics:

Oracle AI Vector Search Professional Sample Questions (Q17-Q22):

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