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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.

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Oracle AI Vector Search Professional Sample Questions (Q57-Q62):

NEW QUESTION # 57
What is the first step in setting up the practice environment for Select AI?

A. Optionally create an OCI compartment
B. Create a new user account with elevated privileges
C. Drop any compartment that does not use OCI Generative AI
D. Create a policy to enable access to OCI Generative AI

Answer: A

Explanation:
Select AI in Oracle Database 23ai enables natural language queries by integrating with OCI Generative AI services. The first step in setting up the practice environment is to optionally create an OCI compartment (A), which organizes and isolates resources in Oracle Cloud Infrastructure (OCI). This is foundational because subsequent steps-like defining policies or configuring the Autonomous Database-depend on a compartment structure, though an existing compartment can be reused, making it optional. Creating a policy (B) is a subsequent step to grant access to OCIGenerative AI, requiring a compartment first. Dropping compartments (C) is irrelevant and disruptive. Creating a user account (D) is not specified as the initial step in Select AI setup. Oracle's Select AI documentation lists compartment setup as the starting point in OCI configuration.

NEW QUESTION # 58
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. Relational filtering combined with an exact search
B. Exact similarity search with a high target accuracy setting
C. Approximate similarity search with a low target accuracy setting
D. Exact similarity search using flat 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 # 59
Which operation is NOT permitted on tables containing VECTOR columns?

A. JOIN ON VECTOR columns
B. UPDATE
C. DELETE
D. SELECT

Answer: A

Explanation:
In Oracle 23ai, tables with VECTOR columns support standard DML operations: SELECT (A) retrieves data, UPDATE (B) modifies rows, and DELETE (C) removes rows. However, JOIN ON VECTOR columns (D) is not permitted because VECTOR isn't a relational type for equality comparison; it's for similarity search (e.g., via VECTOR_DISTANCE). Joins must use non-VECTOR columns. Oracle's SQL reference restricts VECTOR to specific operations, excluding direct joins.

NEW QUESTION # 60
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?

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

Answer: A

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 # 61
What is the significance of using local ONNX models for embedding within the database?

A. Support for legacy SQL*Plus clients
B. Reduced embedding dimensions for faster processing
C. Improved accuracy compared to external models
D. Enhanced security because data remains within the database

Answer: D

Explanation:
Using local ONNX (Open Neural Network Exchange) models for embedding within Oracle Database 23ai means loading pre-trained models (e.g., via DBMS_VECTOR) into the database to generate vectors internally, rather than relying on external APIs or services. The primary significance is enhanced security (D): sensitive data (e.g., proprietary documents) never leaves the database, avoiding exposure to external networks or third-party providers. This aligns with enterprise needs for data privacy and compliance (e.g., GDPR), as the embedding process-say, converting "confidential report" to a vector-occurs within Oracle's secure environment, leveraging its encryption and access controls.
Option A (SQLPlus support) is irrelevant; ONNX integration is about AI functionality, not legacy client compatibility-SQLPlus can query vectors regardless. Option B (improved accuracy) is misleading; accuracy depends on the model's training, not its location-local vs. external models could be identical (e.g., same BERT variant). Option C (reduced dimensions) is a misconception; dimensionality is model-defined (e.g., 768 for BERT), not altered by locality-processing speed might improve due to reduced latency, but that's secondary. Security is the standout benefit, as Oracle's documentation emphasizes in-database processing to minimize data egress risks, a critical consideration for RAG or Select AI workflows where private data fuels LLMs. Without this, external calls could leak context, undermining trust in AI applications.

NEW QUESTION # 62
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Oracle AI Vector Search Professional Sample Questions (Q57-Q62):

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