Pass 1Z0-184-25 Exam with Valid 1Z0-184-25 Upgrade Dumps by Test4Engine

If you are going to take Oracle 1Z0-184-25 certification exam, it is essential to use 1Z0-184-25 training materials. If you are looking for reference materials without a clue, stop！If you don't know what materials you should use, you can try Test4Engine Oracle 1Z0-184-25 exam dumps. The hit rate of the dumps is very high, which guarantees you can pass your exam with ease at the first attempt. Test4Engine Oracle 1Z0-184-25 Practice Test dumps can determine accurately the scope of the examination compared with other exam materials, which can help you improve efficiency of study and help you well prepare for 1Z0-184-25 exam.

Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
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	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 3	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.

>> 1Z0-184-25 Upgrade Dumps <<

1Z0-184-25 Valid Braindumps Book - Latest Braindumps 1Z0-184-25 Ppt

As the authoritative provider of 1Z0-184-25 actual exam, we always pursue high pass rate compared with our peers to gain more attention from those potential customers. We guarantee that if you follow the guidance of our 1Z0-184-25 learning materials, you will pass the exam without a doubt and get a certificate. Our 1Z0-184-25 Exam Practice is carefully compiled after many years of practical effort and is adaptable to the needs of the 1Z0-184-25 exam.

Oracle AI Vector Search Professional Sample Questions (Q15-Q20):

NEW QUESTION # 15
What is created to facilitate the use of OCI Generative AI with Autonomous Database?

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

Answer: B

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 # 16
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 # 17
What is the purpose of the VECTOR_DISTANCE function in Oracle Database 23ai similarity search?

A. To group vectors by their exact scores
B. To fetch rows that match exact vector embeddings
C. To calculate the distance between vectors using a specified metric
D. To create vector indexes for efficient searches

Answer: C

Explanation:
The VECTOR_DISTANCE function in Oracle 23ai (D) computes the distance between two vectors using a specified metric (e.g., COSINE, EUCLIDEAN), enabling similarity search by quantifying proximity. It doesn't fetch exact matches (A); it measures similarity. Index creation (B) is handled by CREATE INDEX, not this function. Grouping (C) requires additional SQL (e.g., GROUP BY), not VECTOR_DISTANCE's role. Oracle's SQL reference defines it as the core tool for distance calculation in vector queries.

NEW QUESTION # 18
Which function is used to generate vector embeddings within an Oracle database?

A. DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS
B. DBMS_VECTOR_CHAIN.UTL_TO_TEXT
C. DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT
D. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS

Answer: A

Explanation:
In Oracle 23ai, the DBMS_VECTOR_CHAIN package provides utilities for vector workflows. UTL_TO_EMBEDDINGS (C) generates vector embeddings from text within the database, typically using an ONNX model, supporting RAG and search applications. UTL_TO_CHUNKS (A) splits text, not generates embeddings. UTL_TO_TEXT (B) converts documents to text, a preprocessing step. UTL_TO_GENERATE_TEXT (D) doesn't exist; text generation is handled by LLMs, not this package. Oracle's documentation identifies UTL_TO_EMBEDDINGS as the embedding creation function in PL/SQL workflows.

NEW QUESTION # 19
What is a key characteristic of HNSW vector indexes?

A. They require exact match for searches
B. They are hierarchical with multilayered connections
C. They use hash-based clustering
D. They are disk-based structures

Answer: B

Explanation:
HNSW (Hierarchical Navigable Small World) indexes in Oracle 23ai (A) are characterized by a hierarchical structure with multilayered connections, enabling efficient approximate nearest neighbor (ANN) searches. This graph-based approach connects vectors across levels, balancing speed and accuracy. They don't require exact matches (B); they're designed for approximate searches. They're memory-optimized, not solely disk-based (C), though persisted to disk. Hash-based clustering (D) relates to other methods (e.g., LSH), not HNSW. Oracle's documentation highlights HNSW's hierarchical nature as key to its performance.

NEW QUESTION # 20
......

Test4Engine Oracle 1Z0-184-25 exam information are cheap and fine. We use simulation questions and answers dedication to our candidates with ultra-low price and high quality. We sincerely hope that you can pass the exam. We provide you with a convenient online service to resolve any questions about Oracle 1Z0-184-25 Exam Questions for you.

1Z0-184-25 Valid Braindumps Book: https://www.test4engine.com/1Z0-184-25_exam-latest-braindumps.html

मराठी गृहिणी

Mark Ford Mark Ford

Biography

Pass 1Z0-184-25 Exam with Valid 1Z0-184-25 Upgrade Dumps by Test4Engine

Oracle 1Z0-184-25 Exam Syllabus Topics:

1Z0-184-25 Valid Braindumps Book - Latest Braindumps 1Z0-184-25 Ppt

Oracle AI Vector Search Professional Sample Questions (Q15-Q20):

मराठी गृहिणी

Quick Links

Imp. Links

Weekly Newsletter