ORACLE - PASS-SURE 1Z0-184-25 - ORACLE AI VECTOR SEARCH PROFESSIONAL LATEST DUMPS PPT

Oracle - Pass-Sure 1Z0-184-25 - Oracle AI Vector Search Professional Latest Dumps Ppt

Oracle - Pass-Sure 1Z0-184-25 - Oracle AI Vector Search Professional Latest Dumps Ppt

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Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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.
Topic 3
  • 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 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.
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 (Q44-Q49):

NEW QUESTION # 44
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 empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries
  • C. 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
  • D. 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

Answer: B

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 # 45
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. oci
  • B. oracledb
  • C. json
  • D. sentence_transformers

Answer: D

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 # 46
What is the primary function of AI Smart Scan in Exadata System Software 24ai?

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

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 # 47
You want to quickly retrieve the top-10 matches for a query vector from a dataset of billions of vectors, prioritizing speed over exact accuracy. What is the best approach?

  • A. Exact similarity search using flat search
  • B. Exact similarity search with a high target accuracy setting
  • C. Approximate similarity search with a low target accuracy setting
  • D. Relational filtering combined with an exact search

Answer: C

Explanation:
For speed over accuracy with billions of vectors, approximate similarity search (ANN) with a low target accuracy setting (B) (e.g., 70%) uses indexes like HNSW or IVF, probing fewer vectors to return top-10 matches quickly. Exact flat search (A) scans all vectors, too slow for billions. Relational filtering with exact search (C) adds overhead without speed gains. Exact search with high accuracy (D) maximizes precision but sacrifices speed. Oracle's documentation recommends ANN for large-scale, speed-focused queries.


NEW QUESTION # 48
How does an application use vector similarity search to retrieve relevant information from a database, and how is this information then integrated into the generation process?

  • A. Clusters similar text chunks and randomly selects one from the most relevant cluster
  • B. Converts the question to keywords, searches for matches, and inserts the text into the response
  • C. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM
  • D. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt

Answer: D

Explanation:
In Oracle 23ai's RAG framework, vector similarity search (A) encodes a user question and database chunks into vectors (e.g., via VECTOR_EMBEDDING), computes similarity (e.g., cosine via VECTOR_DISTANCE), and retrieves the most relevant chunks. These are then included in the LLM prompt, augmenting its response with context. Training a separate LLM (B) is not RAG; RAG uses existing models. Keyword search (C) is traditional, not vector-based, and less semantic. Clustering and random selection (D) lacks precision and isn't RAG's approach. Oracle's documentation describes this encode-search-augment process as RAG's core mechanism.


NEW QUESTION # 49
......

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