2025 Fantastic Hot 1Z0-184-25 Spot Questions Help You Pass 1Z0-184-25 Easily

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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	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	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 4	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.

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2025 Perfect 1Z0-184-25: Hot Oracle AI Vector Search Professional Spot Questions

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Oracle AI Vector Search Professional Sample Questions (Q23-Q28):

NEW QUESTION # 23
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 truncates the vector to fit the defined dimensions
B. The insert operation fails, and an error message is thrown
C. The database pads the vector with zeros to match the defined dimensions
D. The database ignores the defined dimensions and inserts the vector as is

Answer: B

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 # 24
You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?

A. The specific distance algorithm employed for vector comparisons
B. Regularly updating vector embeddings to reflect changes in the source data
C. Using the same embedding model for both vector creation and similarity search
D. The physical storage location of the vector data

Answer: C

Explanation:
In Oracle Database 23ai, vector search accuracy hinges on the consistency of the embedding model. The VECTOR data type stores embeddings as fixed-dimensional arrays, and similarity searches (e.g., using VECTOR_DISTANCE) assume that all vectors-stored and query-are generated by the same model. This ensures they occupy the same semantic space, making distance calculations meaningful. Regular updates (B) maintain data freshness, but if the model changes, integrity is compromised unless all embeddings are regenerated consistently. The distance algorithm (C) (e.g., cosine, Euclidean) defines how similarity is measured but relies on consistent embeddings; an incorrect model mismatch undermines any algorithm. Physical storage location (D) affects performance, not integrity. Oracle's documentation stresses model consistency as a prerequisite for reliable vector search within its native capabilities.

NEW QUESTION # 25
What is the function of the COSINE parameter in the SQL query used to retrieve similar vectors?
topk = 3
sql = f"""select payload, vector_distance(vector, :vector, COSINE) as score from {table_name} order by score fetch approximate {topk} rows only"""

A. It filters out vectors with a cosine similarity below a certain threshold
B. It indicates that the cosine distance metric should be used to measure similarity between vectors
C. It specifies the type of vector encoding used in the database
D. It converts the vectors to a format compatible with the SQL database

Answer: B

Explanation:
In Oracle Database 23ai, the VECTOR_DISTANCE function calculates the distance between two vectors using a specified metric. The COSINE parameter in the query (vector_distance(vector, :vector, COSINE)) instructs the database to use the cosine distance metric (C) to measure similarity. Cosine distance, defined as 1 - cosine similarity, is ideal for high-dimensional vectors (e.g., text embeddings) as it focuses on angular separation rather than magnitude. It doesn't filter vectors (A); filtering requires additional conditions (e.g., WHERE clause). It doesn't convert vector formats (B); vectors are already in the VECTOR type. It also doesn't specify encoding (D), which is defined during vector creation (e.g., FLOAT32). Oracle's documentation confirms COSINE as one of the supported metrics for similarity search.

NEW QUESTION # 26
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?

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

Answer: C

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 # 27
Which PL/SQL package is primarily used for interacting with Generative AI services in Oracle Database 23ai?

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

Answer: C

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 # 28
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Revised and updated according to the syllabus changes and all the latest developments in theory and practice, our Oracle AI Vector Search Professional dumps are highly relevant to what you actually need to get through the certifications tests. Moreover they impart you information in the format of 1Z0-184-25 Questions and answers that is actually the format of your real certification test. Hence not only you get the required knowledge but also find the opportunity to practice real exam scenario. For consolidation of your learning, our Oracle AI Vector Search Professional dumps PDF file also provide you sets of practice questions and answers. Doing them again and again, you enrich your knowledge and maximize chances of an outstanding exam success.

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2025 Fantastic Hot 1Z0-184-25 Spot Questions Help You Pass 1Z0-184-25 Easily

Oracle 1Z0-184-25 Exam Syllabus Topics:

2025 Perfect 1Z0-184-25: Hot Oracle AI Vector Search Professional Spot Questions

Oracle AI Vector Search Professional Sample Questions (Q23-Q28):

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