USEFUL 1Z0-184-25 STUDY GUIDE PDF COVERS THE ENTIRE SYLLABUS OF 1Z0-184-25

Useful 1Z0-184-25 Study Guide Pdf Covers the Entire Syllabus of 1Z0-184-25

Useful 1Z0-184-25 Study Guide Pdf Covers the Entire Syllabus of 1Z0-184-25

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The Oracle AI Vector Search Professional (1Z0-184-25) dumps PDF file can be used from any location and at any time. Furthermore, you can take print of Oracle Questions PDF to do an off-screen study. The web-based 1Z0-184-25 practice exam can be taken via the internet from any browser like Firefox, Safari, Opera, MS Edge, Internet Explorer, and Chrome. You don't need to install any excessive plugins and software to take this Oracle AI Vector Search Professional (1Z0-184-25) practice test.

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
  • 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 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.

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

NEW QUESTION # 17
What is the significance of using local ONNX models for embedding within the database?

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

Answer: A

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 # 18
Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?

  • A. Because of the complexity of vector data, Data Pump requires a specialized plug-in to handle the export and import operations involving vector data types
  • B. Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications
  • C. Data Pump only exports and imports vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database
  • D. Data Pump treats vector embeddings as regular text strings, which can lead to data corruption or loss of precision when transferring vector data for vector search

Answer: B

Explanation:
Oracle Data Pump in 23ai natively supports the VECTOR data type (C), allowing export and import of tables with vector columns without conversion or plug-ins. This facilitates vector search application migrations, preserving dimensional and format integrity (e.g., FLOAT32). BLOB storage (A) isn't required; VECTOR is a distinct type. Data Pump doesn't treat vectors as text (B), avoiding corruption; it handles them as structured arrays. No specialized plug-in (D) is needed; native support is built-in. Oracle's Data Pump documentation confirms seamless handling of VECTOR data.


NEW QUESTION # 19
Which SQL statement correctly adds a VECTOR column named "v" with 4 dimensions and FLOAT32 format to an existing table named "my_table"?

  • A. ALTER TABLE my_table ADD v VECTOR(4, FLOAT32)
  • B. UPDATE my_table SET v = VECTOR(4, FLOAT32)
  • C. ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32))
  • D. ALTER TABLE my_table MODIFY (v VECTOR(4, FLOAT32))

Answer: C

Explanation:
To add a new column to an existing table, Oracle uses the ALTER TABLE statement with the ADD clause. Option B, ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32)), correctly specifies the column name "v", the VECTOR type, and its attributes (4 dimensions, FLOAT32 precision) within parentheses, aligning with Oracle's DDL syntax for VECTOR columns. Option A uses MODIFY, which alters existing columns, not adds new ones, making it incorrect here. Option C uses UPDATE, a DML statement for updating data, not a DDL operation for schema changes. Option D omits parentheses around the VECTOR specification, which is syntactically invalid as Oracle requires dimensions and format to be enclosed. The SQL Language Reference confirms this syntax for adding VECTOR columns.


NEW QUESTION # 20
Which of the following actions will result in an error when using VECTOR_DIMENSION_COUNT() in Oracle Database 23ai?

  • A. Providing a vector with a dimensionality that exceeds the specified dimension count
  • B. Providing a vector with duplicate values for its components
  • C. Using a vector with a data type that is not supported by the function
  • D. Calling the function on a vector that has been created with TO_VECTOR()

Answer: C

Explanation:
The VECTOR_DIMENSION_COUNT() function in Oracle 23ai returns the number of dimensions in a VECTOR-type value (e.g., 512 for VECTOR(512, FLOAT32)). It's a metadata utility, not a validator of content or structure beyond type compatibility. Option B-using a vector with an unsupported data type-causes an error because the function expects a VECTOR argument; passing, say, a VARCHAR2 or NUMBER instead (e.g., '1,2,3' or 42) triggers an ORA-error (e.g., ORA-00932: inconsistent datatypes). Oracle enforces strict typing for vector functions.
Option A (exceeding specified dimensions) is a red herring; the function reports the actual dimension count of the vector, not the column's defined limit-e.g., VECTOR_DIMENSION_COUNT(TO_VECTOR('[1,2,3]')) returns 3, even if the column is VECTOR(2), as the error occurs at insertion, not here. Option C (duplicate values, like [1,1,2]) is valid; the function counts dimensions (3), ignoring content. Option D (using TO_VECTOR()) is explicitly supported; VECTOR_DIMENSION_COUNT(TO_VECTOR('[1.2, 3.4]')) returns 2 without issue. Misinterpreting this could lead developers to over-constrain data prematurely-B's type mismatch is the clear error case, rooted in Oracle's vector type system.


NEW QUESTION # 21
Which is NOT a feature or capability related to AI and Vector Search in Exadata?

  • A. Loading Vector Data using SQL*Loader
  • B. Native Support for Vector Search Only within the Database Server
  • C. Vector Replication with GoldenGate
  • D. AI Smart Scan

Answer: B

Explanation:
Exadata in Oracle Database 23ai enhances AI and vector search capabilities. Vector Replication with GoldenGate (B) supports real-time vector data distribution. SQL*Loader (C) loads vector data into VECTOR columns. AI Smart Scan (D) accelerates AI workloads using Exadata's storage optimizations. However, "Native Support for Vector Search Only within the Database Server" (A) is not a feature; vector search is natively supported across Exadata's architecture, leveraging both database and storage layers (e.g., via Smart Scan), not restricted to the server alone. This option misrepresents Exadata's distributed capabilities, making it the correct "NOT" answer.


NEW QUESTION # 22
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