TEST ORACLE 1Z0-184-25 ANSWERS, 1Z0-184-25 VALID TEST PAPERS

Test Oracle 1Z0-184-25 Answers, 1Z0-184-25 Valid Test Papers

Test Oracle 1Z0-184-25 Answers, 1Z0-184-25 Valid Test Papers

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Tags: Test 1Z0-184-25 Answers, 1Z0-184-25 Valid Test Papers, New 1Z0-184-25 Exam Notes, New Study 1Z0-184-25 Questions, Latest 1Z0-184-25 Guide Files

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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
  • 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.

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Oracle AI Vector Search Professional Sample Questions (Q50-Q55):

NEW QUESTION # 50
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

  • A. To calculate vector dimensions
  • B. To calculate vector distances
  • C. To serialize vectors into a string
  • D. To generate a single vector embedding for data

Answer: D


NEW QUESTION # 51
In the following Python code, what is the significance of prepending the source filename to each text chunk before storing it in the vector database?
bash
CollapseWrapCopy
docs = [{"text": filename + "|" + section, "path": filename} for filename, sections in faqs.items() for section in sections]
# Sample the resulting data
docs[:2]

  • A. It speeds up the vectorization process by providing a unique identifier for each chunk
  • B. It helps differentiate between chunks from different files but has no impact on vectorization
  • C. It improves the accuracy of the LLM by providing additional training data
  • D. It preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file

Answer: D

Explanation:
Prepending the filename to each text chunk (e.g., filename + "|" + section) in the Python code (A) preserves contextual metadata, linking each chunk-and its resulting vector-to its source file. This aids retrieval in RAG applications by allowing the application to trace back to the original document, enhancing response context (e.g., "from Book1"). While it differentiates chunks (B), its impact goes beyond identification, affecting retrieval usability. It doesn't speed up vectorization (C); embedding models process text regardless of prefixes. It also doesn't train the LLM (D); it's metadata for retrieval, not training data. Oracle's RAG examples emphasize metadata preservation for context-aware responses.


NEW QUESTION # 52
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?

  • A. OCI Generative AI
  • B. OCI Language
  • C. OCI Data Science
  • D. OCI Vision

Answer: A

Explanation:
Select AI in Oracle Database 23ai integrates with OCI Generative AI (B) to process natural language queries and generate context-aware responses using large language models (LLMs). OCI Language (A) focuses on text analysis (e.g., sentiment, entity recognition), not generative tasks. OCI Vision (C) handles image processing, unrelated to Select AI's text-based functionality. OCI Data Science (D) supports model development, not direct integration with Select AI. Oracle's documentation explicitly names OCI Generative AI as the integrated service for Select AI's LLM capabilities.


NEW QUESTION # 53
How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?

  • A. By encrypting all communication between the Autonomous Database and OCI Generative AI using TLS/SSL protocols
  • B. By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service
  • C. By requiring users to manually enter their OCI API keys each time they execute a natural language query
  • D. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials

Answer: D

Explanation:
In Oracle Database 23ai's Select AI, security between the Autonomous Database and OCI Generative AI is managed using Resource Principals (B). This mechanism allows the database instance to authenticate itself to OCI services without hardcoding credentials, enhancing security by avoiding exposure of sensitive keys. TLS/SSL encryption (A) is used for data-in-transit security, but it's a complementary layer, not the primary management method. A VPN tunnel (C) is unnecessary within OCI's secure infrastructure and not specified for Select AI. Manual API key entry (D) is impractical and insecure for automated database interactions. Oracle's documentation on Select AI highlights Resource Principals as the secure, scalable authentication method.


NEW QUESTION # 54
What is the primary function of an embedding model in the context of vector search?

  • A. To execute similarity search operations within a database
  • B. To define the schema for a vector database
  • C. To store vectors in a structured format for efficient retrieval
  • D. To transform text or data into numerical vector representations

Answer: D

Explanation:
An embedding model in the context of vector search, such as those used in Oracle Database 23ai, is fundamentally a machine learning construct (e.g., BERT, SentenceTransformer, or an ONNX model) designed to transform raw data-typically text, but also images or other modalities-into numerical vector representations (C). These vectors, stored in the VECTOR data type, encapsulate semantic meaning in a high-dimensional space where proximity reflects similarity. For instance, the word "cat" might be mapped to a 512-dimensional vector like [0.12, -0.34, ...], where its position relative to "dog" indicates relatedness. This transformation is the linchpin of vector search, enabling mathematical operations like cosine distance to find similar items.
Option A (defining schema) misattributes a database design role to the model; schema is set by DDL (e.g., CREATE TABLE with VECTOR). Option B (executing searches) confuses the model with database functions like VECTOR_DISTANCE, which use the embeddings, not create them. Option D (storing vectors) pertains to the database's storage engine, not the model's function-storage is handled by Oracle's VECTOR type and indexes (e.g., HNSW). The embedding model's role is purely generative, not operational or structural. In practice, Oracle 23ai integrates this via VECTOR_EMBEDDING, which calls the model to produce vectors, underscoring its transformative purpose. Misunderstanding this could lead to conflating data preparation with query execution, a common pitfall for beginners.


NEW QUESTION # 55
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