Blog

Derek Gray Derek Gray

0 Course Enrolled • 0 Course Completed

Biography

Practical 1Z0-184-25 Information & 1Z0-184-25 Latest Exam Format

The precision and accuracy of PassCollection’s dumps are beyond other exam materials. They are time-tested and approved by the veteran professionals who recommend them as the easiest way-out for 1Z0-184-25 certification tests. 1Z0-184-25 Exam Materials constantly updated by our experts, enhancing them in line with the changing standards of real exam criteria. Therefore, our 1Z0-184-25 dumps prove always compatible to your academic requirement.

Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 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

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.

>> Practical 1Z0-184-25 Information <<

1Z0-184-25 Latest Exam Format, 1Z0-184-25 New Study Plan

We try to offer the best 1Z0-184-25 exam braindumps to our customers. First of all, in order to give users a better experience, we have been updating the system of 1Z0-184-25 simulating exam to meet the needs of more users. After the new version appears, we will also notify the user at the first time. Second, in terms of content, we guarantee that the content provided by our 1Z0-184-25 Study Materials is the most comprehensive.

Oracle AI Vector Search Professional Sample Questions (Q44-Q49):

NEW QUESTION # 44
Which PL/SQL package is primarily used for interacting with Generative AI services in Oracle Database 23ai?

A. DBMS_AI
B. DBMS_ML
C. DBMS_VECTOR_CHAIN
D. DBMS_GENAI

Answer: A

Explanation:
Oracle Database 23ai introduces DBMS_AI as the primary PL/SQL package for interacting with Generative AI services, such as OCI Generative AI, enabling features like natural language query processing (e.g., Select AI) and AI-driven insights. DBMS_ML (B) focuses on machine learning model training and management, not generative AI. DBMS_VECTOR_CHAIN (C) supports vector processing workflows (e.g., document chunking, embedding), but it's not the main interface for generative AI services. DBMS_GENAI (D) is not a recognized package in 23ai documentation. DBMS_AI's role is highlighted in Oracle's AI integration features for 23ai.

NEW QUESTION # 45
What is the purpose of the VECTOR_DISTANCE function in Oracle Database 23ai similarity search?

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

Answer: D

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 # 46
When generating vector embeddings outside the database, what is the most suitable option for storing the embeddings for later use?

A. In the database as BLOB (Binary Large Object) data
B. In a binary FVEC file with the relational data in a CSV file
C. In a CSV file
D. In a dedicated vector database

Answer: D

Explanation:
When vector embeddings are generated outside the database, the storage choice must balance efficiency, scalability, and usability for similarity search. A CSV file (A) is simple and human-readable but inefficient for large-scale vector operations due to text parsing overhead and lack of indexing support. A binary FVEC file (B) offers a compact format for vectors, reducing storage size and improving read performance, but separating relational data into a CSV complicates integration and querying, making it suboptimal for unified workflows. Storing embeddings as BLOBs in a relational database (C) integrates well with structured data and supports SQL access, but it lacks the specialized indexing (e.g., HNSW, IVF) and query optimizations that dedicated vector databases provide. A dedicated vector database (D), such as Milvus or Pinecone (or Oracle 23ai's vector capabilities if internal), is purpose-built for high-dimensional vectors, offering efficient storage, advanced indexing, and fast approximate nearest neighbor (ANN) searches. For external generation scenarios, where embeddings are not immediately integrated into Oracle 23ai, a dedicated vector database is the most suitable due to its performance and scalability advantages. Oracle's AI Vector Search documentation indirectly supports this by emphasizing optimized vector storage for search efficiency, though it focuses on in-database solutions.

NEW QUESTION # 47
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 MODIFY (v VECTOR(4, FLOAT32))
D. ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32))

Answer: D

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 # 48
What is the primary function of AI Smart Scan in Exadata System Software 24ai?

A. To accelerate AI workloads by leveraging Exadata RDMA Memory (XRMEM), Exadata Smart Cache, and on-storage processing
B. To automatically optimize database queries for improved performance
C. To provide real-time monitoring and diagnostics for AI applications

Answer: A

Explanation:
AI Smart Scan in Exadata System Software 24ai (B) accelerates AI workloads, including vector search, by offloading processing to storage servers using Exadata's RDMA Memory (XRMEM), Smart Cache, and on-storage capabilities. This enhances performance for large-scale vector operations. Real-time monitoring (A) isn't its focus; that's for management tools. Queryoptimization (C) is a general Exadata feature (Smart Scan), but AI Smart Scan specifically targets AI tasks. Oracle's 24ai documentation emphasizes its role in speeding up AI computations.

NEW QUESTION # 49
......

In the information society, everything is changing rapidly. In order to allow users to have timely access to the latest information, our 1Z0-184-25 real exam has been updated. Our update includes not only the content but also the functionality of the system. The content of the 1Z0-184-25 training guide is the real questions and answers which are always kept to be the latest according to the efforts of the professionals. And we apply the newest technologies to the system of our 1Z0-184-25 exam questions.

1Z0-184-25 Latest Exam Format: https://www.passcollection.com/1Z0-184-25_real-exams.html

Cookie	Duration	Description
cookielawinfo-checkbox-analytics	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional	11 months	The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy	11 months	The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.

Derek Gray Derek Gray

Biography

COOKIE NOTICE