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Oracle - Updated 1Z0-184-25 Exam Assessment

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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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.
Topic 5	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.

Oracle AI Vector Search Professional Sample Questions (Q37-Q42):

NEW QUESTION # 37
What happens when querying with an IVF index if you increase the value of the NEIGHBOR_PARTITIONS probes parameter?

A. More partitions are probed, improving accuracy, but also increasing query latency
B. Index creation time is reduced
C. The number of centroids decreases
D. Accuracy decreases

Answer: A

Explanation:
The NEIGHBOR_PARTITIONS parameter in Oracle 23ai's IVF index controls how many partitions are probed during a query. Increasing this value examines more clusters, raising theprobability of finding relevant vectors, thus improving accuracy (recall). However, this increases computational effort, leading to higher query latency-a classic ANN trade-off. The number of centroids (A) is fixed during index creation and unaffected by query parameters. Accuracy does not decrease (B); it improves. Index creation time (C) is unrelated to query-time settings. Oracle's documentation on IVF confirms that NEIGHBOR_PARTITIONS directly governs this accuracy-latency balance.

NEW QUESTION # 38
What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?

A. Load ONNX Model, Vectorize Question, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
B. Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, Perform Vector Search, Generate Output
C. Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output
D. Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output

Answer: C

Explanation:
Building a RAG application in Oracle 23ai using PL/SQL follows a logical sequence: (1) Load Document (e.g., via SQL*Loader) into the database; (2) Split Text into Chunks (e.g., DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS) to manage token limits; (3) Load ONNX Model (e.g., via DBMS_VECTOR) for embedding generation; (4) Create Embeddings (e.g., UTL_TO_EMBEDDINGS) for the chunks; (5) Vectorize Question (using the same model) when a query is received; (6) Perform Vector Search (e.g., VECTOR_DISTANCE) to find relevant chunks; (7) Generate Output (e.g., via DBMS_AI with an LLM). Option B matches this flow. A starts with the model prematurely. C prioritizes the question incorrectly. D is close but loads the model too early. Oracle's RAG workflow documentation outlines this document-first approach.

NEW QUESTION # 39
What is the purpose of the VECTOR_DISTANCE function in Oracle Database 23ai similarity search?

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

Answer: A

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 # 40
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 database ignores the defined dimensions and inserts the vector as is
C. The insert operation fails, and an error message is thrown
D. The database pads the vector with zeros to match the defined dimensions

Answer: C

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 # 41
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_CHAIN.UTL_TO_TEXT
C. DBMS_VECTOR.CONVERT_TO_TEXT
D. DBMS_VECTOR.TEXT_TO_PLAIN

Answer: B

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 # 42
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100% Pass Fantastic 1Z0-184-25 - Oracle AI Vector Search Professional Exam Assessment

Oracle - Updated 1Z0-184-25 Exam Assessment

Oracle 1Z0-184-25 Exam Syllabus Topics:

Oracle AI Vector Search Professional Sample Questions (Q37-Q42):

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