Sam Cook Sam Cook's Profile Page

Sam Cook Sam Cook

0 Course Enrolled • 0 Course Completed

Biography

1Z0-184-25 Latest Exam Experience - 1Z0-184-25 Practice Tests

P.S. Free 2025 Oracle 1Z0-184-25 dumps are available on Google Drive shared by Fast2test: https://drive.google.com/open?id=11mKku1Y-uPw6WPx11CH3XvJnBN1UWt5j

Our 1Z0-184-25 study tool prepared by our company has now been selected as the secret weapons of customers who wish to pass the exam and obtain relevant certification. If you are agonizing about how to pass the exam and to get the Oracle certificate, now you can try our learning materials. Our reputation is earned by high-quality of our learning materials. Once you choose our training materials, you chose hope. Our learning materials are based on the customer's point of view and fully consider the needs of our customers. If you follow the steps of our 1Z0-184-25 Exam Questions, you can easily and happily learn and ultimately succeed in the ocean of learning. Next, I will detail the relevant information of our learning materials so that you can have a better understanding of our 1Z0-184-25 guide training.

Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

Understand Vector Fundamentals: This section of the exam measures the skills of Data Engineers in working with vector data types for storing embeddings and enabling semantic queries. It covers vector distance functions and metrics used in AI vector search. Candidates must demonstrate proficiency in performing DML and DDL operations on vectors to manage data efficiently.

Topic 4

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 Latest Exam Experience <<

1Z0-184-25 Practice Tests, New 1Z0-184-25 Dumps Questions

Are you planning to appear in the Oracle AI Vector Search Professional (1Z0-184-25) certification test and need to know where to get updated practice questions? Then you are at the right place because Oracle AI Vector Search Professional (1Z0-184-25) has made the learning material for the applicants to prepare successfully for the certfication exam in a short time.

Oracle AI Vector Search Professional Sample Questions (Q60-Q65):

NEW QUESTION # 60
In Oracle Database 23ai, which SQL function calculates the distance between two vectors using the Euclidean metric?

A. L2_DISTANCE
B. COSINE_DISTANCE
C. HAMMING_DISTANCE
D. L1_DISTANCE

Answer: A

Explanation:
In Oracle Database 23ai, vector distance calculations are primarily handled by the VECTOR_DISTANCE function, which supports multiple metrics (e.g., COSINE, EUCLIDEAN) specified as parameters (e.g., VECTOR_DISTANCE(v1, v2, EUCLIDEAN)). However, the question implies distinct functions, a common convention in some databases or libraries, and Oracle's documentation aligns L2_DISTANCE (B) with the Euclidean metric. L2 (Euclidean) distance is the straight-line distance between two points in vector space, computed as √∑(xi - yi)², where xi and yi are vector components. For example, for vectors [1, 2] and [4, 6], L2 distance is √((1-4)² + (2-6)²) = √(9 + 16) = 5.
Option A, L1_DISTANCE, represents Manhattan distance (∑|xi - yi|), summing absolute differences-not Euclidean. Option C, HAMMING_DISTANCE, counts differing bits, suited for binary vectors (e.g., INT8), not continuous Euclidean spaces typically used with FLOAT32 embeddings. Option D, COSINE_DISTANCE (1 - cosine similarity), measures angular separation, distinct from Euclidean's magnitude-inclusive approach. While VECTOR_DISTANCE is the general function in 23ai, L2_DISTANCE may be an alias or a contextual shorthand in some Oracle AI examples, reflecting Euclidean's prominence in geometric similarity tasks. Misinterpreting this could lead to choosing COSINE for spatial tasks where magnitude matters, skewing results. Oracle's vector search framework supports Euclidean via VECTOR_DISTANCE, but B aligns with the question's phrasing.

NEW QUESTION # 61
Which PL/SQL function converts documents such as PDF, DOC, JSON, XML, or HTML to plain text?

A. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS
B. DBMS_VECTOR.TEXT_TO_PLAIN
C. DBMS_VECTOR_CHAIN.UTL_TO_TEXT
D. DBMS_VECTOR.CONVERT_TO_TEXT

Answer: C

Explanation:
In Oracle Database 23ai, DBMS_VECTOR_CHAIN.UTL_TO_TEXT is the PL/SQL function that converts documents in formats like PDF, DOC, JSON, XML, or HTML into plain text, a key step in preparing data for vectorization in RAG workflows. DBMS_VECTOR.TEXT_TO_PLAIN (A) is not a valid function. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS (C) splits text into smaller segments, not converts documents. DBMS_VECTOR.CONVERT_TO_TEXT (D) does not exist in the documented packages. UTL_TO_TEXT is part of the DBMS_VECTOR_CHAIN package, designed for vector processing pipelines, and is explicitly noted for document conversion in Oracle's documentation.

NEW QUESTION # 62
Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?

A. It primarily aims to optimize the performance and efficiency of LLMs by using advanced data retrieval techniques, thus minimizing response times and reducing computational overhead
B. It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries
C. It allows users to train their own specialized LLMs directly within the Oracle Database environment using their internal data, thereby reducing reliance on external AI providers
D. It enables Large Language Models (LLMs) to access and process real-time data streams from diverse sources to generate the most up-to-date insights

Answer: B

Explanation:
RAG in Oracle Database 23ai combines vector search with LLMs to enhance responses by retrieving relevant private data from the database (e.g., via VECTOR columns) and augmenting LLM prompts. This (A) improves context-awareness and precision, leveraging enterprise-specific data without retraining LLMs. Optimizing LLM performance (B) is a secondary benefit, not the core focus. Training specialized LLMs (C) is not RAG's purpose; it uses existing models. Real-time streaming (D) is possible but not the primary benefit, as RAG focuses on stored data retrieval. Oracle's RAG documentation emphasizes private data integration for better LLM outputs.

NEW QUESTION # 63
What happens when you attempt to insert a vector with an incorrect number of dimensions into a VECTOR column with a defined number of dimensions?

A. The database pads the vector with zeros to match the defined dimensions
B. The database ignores the defined dimensions and inserts the vector as is
C. The database truncates the vector to fit the defined dimensions
D. The insert operation fails, and an error message is thrown

Answer: D

Explanation:
In Oracle Database 23ai, a VECTOR column with a defined dimension count (e.g., VECTOR(4, FLOAT32)) enforces strict dimensional integrity to ensure consistency for similarity search and indexing. Attempting to insert a vector with a mismatched number of dimensions-say, TO_VECTOR('[1.2, 3.4, 5.6]') (3D) into a VECTOR(4)-results in the insert operation failing with an error (D), such as ORA-13199: "vector dimension mismatch." This rigidity protects downstream AI operations; a 3D vector in a 4D column would misalign with indexed data (e.g., HNSW graphs), breaking similarity calculations like cosine distance, which require uniform dimensionality.
Option A (truncation) is tempting but incorrect; Oracle doesn't silently truncate [1.2, 3.4, 5.6] to [1.2, 3.4]-this would discard data arbitrarily, risking semantic loss (e.g., a truncated sentence embedding losing meaning). Option B (padding with zeros) seems plausible-e.g., [1.2, 3.4, 5.6] becoming [1.2, 3.4, 5.6, 0]-but Oracle avoids implicit padding to prevent unintended semantic shifts (zero-padding could alter distances). Option C (ignoring dimensions) only applies to undefined VECTOR columns (e.g., VECTOR without size), not fixed ones; here, the constraint is enforced. The failure (D) forces developers to align data explicitly (e.g., regenerate embeddings), ensuring reliability-a strict but necessary design choice in Oracle's AI framework. In practice, this error prompts debugging upstream data pipelines, avoiding silent failures that could plague production AI systems.

NEW QUESTION # 64
How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?

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

Answer: A

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 # 65
......

There are many certificates for you to get but which kind of certificate is most authorized, efficient and useful? We recommend you the 1Z0-184-25 certificate because it can prove that you are competent in some area and boost outstanding abilities. If you buy our 1Z0-184-25 study materials you will pass the test smoothly and easily. We boost professional expert team to organize and compile the 1Z0-184-25 Training Materials diligently and provide the great service which include the service before and after the sale, the 24-hours online customer servic on our 1Z0-184-25 exam questions.

1Z0-184-25 Practice Tests: https://www.fast2test.com/1Z0-184-25-premium-file.html

BONUS!!! Download part of Fast2test 1Z0-184-25 dumps for free: https://drive.google.com/open?id=11mKku1Y-uPw6WPx11CH3XvJnBN1UWt5j

Sam Cook Sam Cook

Biography

Quick Links

Resources

Supports