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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
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	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.

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

NEW QUESTION # 60
You want to quickly retrieve the top-10 matches for a query vector from a dataset of billions of vectors, prioritizing speed over exact accuracy. What is the best approach?

A. Relational filtering combined with an exact search
B. Exact similarity search using flat search
C. Approximate similarity search with a low target accuracy setting
D. Exact similarity search with a high target accuracy setting

Answer: C

Explanation:
For speed over accuracy with billions of vectors, approximate similarity search (ANN) with a low target accuracy setting (B) (e.g., 70%) uses indexes like HNSW or IVF, probing fewer vectors to return top-10 matches quickly. Exact flat search (A) scans all vectors, too slow for billions. Relational filtering with exact search (C) adds overhead without speed gains. Exact search with high accuracy (D) maximizes precision but sacrifices speed. Oracle's documentation recommends ANN for large-scale, speed-focused queries.

NEW QUESTION # 61
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. Using the same embedding model for both vector creation and similarity search
B. The specific distance algorithm employed for vector comparisons
C. Regularly updating vector embeddings to reflect changes in the source data
D. The physical storage location of the vector data

Answer: A

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 # 62
What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?

A. Both operate identically but differ in memory usage
B. HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions
C. HNSW guarantees accuracy, whereas IVF sacrifices performance for accuracy
D. HNSW is partition-based, whereas IVF uses neighbor graphs for indexing

Answer: B

NEW QUESTION # 63
What is the primary purpose of a similarity search in Oracle Database 23ai?

A. To find exact matches in BLOB data
B. To retrieve the most semantically similar entries using distance metrics between different vectors
C. Optimize relational database operations to compute distances between all data points in a database
D. To group vectors by their exact scores

Answer: B

Explanation:
Similarity search in Oracle 23ai (C) uses vector embeddings in VECTOR columns to retrieve entries semantically similar to a query vector, based on distance metrics (e.g., cosine, Euclidean) via functions like VECTOR_DISTANCE. This is key for AI applications like RAG, finding "close" rather than exact matches. Optimizing relational operations (A) is unrelated; similarity search is vector-specific. Exact matches in BLOBs (B) don't leverage vector semantics. Grouping by scores (D) is a post-processing step, not the primary purpose. Oracle's documentation defines similarity search as retrieving semantically proximate vectors.

NEW QUESTION # 64
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_CHUNKS
D. DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT

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 # 65
......

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1Z0-184-25 Valid Test Vce Free, 1Z0-184-25 Test Quiz

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Oracle 1Z0-184-25 Exam Syllabus Topics:

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

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