RELIABLE ORACLE 1Z0-184-25 EXAM BOOK, 1Z0-184-25 VALID TEST SAMPLE

Reliable Oracle 1Z0-184-25 Exam Book, 1Z0-184-25 Valid Test Sample

Reliable Oracle 1Z0-184-25 Exam Book, 1Z0-184-25 Valid Test Sample

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Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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.

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Oracle AI Vector Search Professional Sample Questions (Q36-Q41):

NEW QUESTION # 36
Which is NOT a feature or capability related to AI and Vector Search in Exadata?

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

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 # 37
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 # 38
What is the default distance metric used by the VECTOR_DISTANCE function if none is specified?

  • A. Euclidean
  • B. Hamming
  • C. Manhattan
  • D. Cosine

Answer: D

Explanation:
The VECTOR_DISTANCE function in Oracle 23ai computes vector distances, and if no metric is specified (e.g., VECTOR_DISTANCE(v1, v2)), it defaults to Cosine (C). Cosine distance (1 - cosine similarity) is widely used for text embeddings due to its focus on angular separation, ignoring magnitude-fitting for normalized vectors from models like BERT. Euclidean (A) measures straight-line distance, not default. Hamming (B) is for binary vectors, rare in 23ai's FLOAT32 context. Manhattan (D) sums absolute differences, less common for embeddings. Oracle's choice of Cosine reflects its AI focus, as documentation confirms, aligning with industry norms for semantic similarity-vital for users assuming defaults in queries.


NEW QUESTION # 39
What is the primary purpose of the DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS package in a RAG application?

  • A. To split a large document into smaller chunks to improve vector quality by minimizing token truncation
  • B. To generate vector embeddings from a text document
  • C. To load a document into the database
  • D. To convert a document into a single, large text string

Answer: A

Explanation:
In Oracle Database 23ai, the DBMS_VECTOR_CHAIN package supports Retrieval Augmented Generation (RAG) workflows by providing utilities for vector processing. The UTL_TO_CHUNKS function specifically splits large documents into smaller, manageable text chunks. This is critical in RAG applications because embedding models (e.g., BERT, ONNX models) have token limits (e.g., 512 tokens). Splitting text minimizes token truncation, ensuring that each chunk retains full semantic meaning, which improves the quality of subsequent vector embeddings and search accuracy. Generating embeddings (A) is handled by functions like VECTOR_EMBEDDING, not UTL_TO_CHUNKS. Loading documents (B) is a separate process (e.g., via SQL*Loader). Converting to a single text string (D) contradicts the chunking purpose and risks truncation. Oracle's documentation on DBMS_VECTOR_CHAIN emphasizes chunking for optimizing vector quality in RAG.


NEW QUESTION # 40
What is created to facilitate the use of OCI Generative AI with Autonomous Database?

  • A. A dedicated OCI compartment
  • B. A secure VPN tunnel
  • C. An AI profile for OCI Generative AI
  • D. A new user account with elevated privileges

Answer: C

Explanation:
To integrate OCI Generative AI with Autonomous Database in Oracle 23ai (e.g., for Select AI), an AI profile (A) is created within the database using DBMS_AI. This profile configures the connection to OCI Generative AI, specifying the LLM and authentication (e.g., Resource Principals). A compartment (B) organizes OCI resources but isn't "created" specifically for this integration; it's a prerequisite. A new user account (C) or VPN tunnel (D) isn't required; security leverages existing mechanisms. Oracle's Select AI setup documentation highlights the AI profile as the key facilitator.


NEW QUESTION # 41
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