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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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.

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Quiz 2025 Oracle 1Z0-184-25: First-grade Oracle AI Vector Search Professional Latest Real Exam

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Oracle AI Vector Search Professional Sample Questions (Q16-Q21):

NEW QUESTION # 16
What is the primary function of AI Smart Scan in Exadata System Software 24ai?

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

Answer: A

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 # 17
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 # 18
What is the primary purpose of a similarity search in Oracle Database 23ai?

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

Answer: B

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 # 19
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?

A. Multivector similarity search with partitioning
B. Approximate similarity search with HNSW indexing and target accuracy of 70%
C. Exact similarity search using a full table scan
D. Approximate similarity search with IVF indexing and target accuracy of 70%

Answer: C

Explanation:
To prioritize accuracy over speed, exact similarity search with a full table scan (C) computes distances between the query vector and all stored vectors, guaranteeing 100% recall without approximation trade-offs. HNSW with 70% target accuracy (A) and IVF with 70% (D) are approximate methods, sacrificing accuracy for speed via indexing (e.g., probing fewer neighbors). Multivector search (B) isn't a standard Oracle 23ai term; partitioning aids scale, not accuracy. Exact search, though slower, ensures maximum accuracy, as per Oracle's vector search options.

NEW QUESTION # 20
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 # 21
......

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1Z0-184-25 Latest Real Exam - Pass 1Z0-184-25 in One Time - 1Z0-184-25 Trustworthy Practice

Oracle 1Z0-184-25 Exam Syllabus Topics:

Quiz 2025 Oracle 1Z0-184-25: First-grade Oracle AI Vector Search Professional Latest Real Exam

Oracle AI Vector Search Professional Sample Questions (Q16-Q21):

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