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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

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

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

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 5

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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Oracle AI Vector Search Professional Sample Questions (Q56-Q61):

NEW QUESTION # 56
Which of the following actions will result in an error when using VECTOR_DIMENSION_COUNT() in Oracle Database 23ai?

A. Using a vector with a data type that is not supported by the function
B. Providing a vector with a dimensionality that exceeds the specified dimension count
C. Calling the function on a vector that has been created with TO_VECTOR()
D. Providing a vector with duplicate values for its components

Answer: A

Explanation:
The VECTOR_DIMENSION_COUNT() function in Oracle 23ai returns the number of dimensions in a VECTOR-type value (e.g., 512 for VECTOR(512, FLOAT32)). It's a metadata utility, not a validator of content or structure beyond type compatibility. Option B-using a vector with an unsupported data type-causes an error because the function expects a VECTOR argument; passing, say, a VARCHAR2 or NUMBER instead (e.g., '1,2,3' or 42) triggers an ORA-error (e.g., ORA-00932: inconsistent datatypes). Oracle enforces strict typing for vector functions.
Option A (exceeding specified dimensions) is a red herring; the function reports the actual dimension count of the vector, not the column's defined limit-e.g., VECTOR_DIMENSION_COUNT(TO_VECTOR('[1,2,3]')) returns 3, even if the column is VECTOR(2), as the error occurs at insertion, not here. Option C (duplicate values, like [1,1,2]) is valid; the function counts dimensions (3), ignoring content. Option D (using TO_VECTOR()) is explicitly supported; VECTOR_DIMENSION_COUNT(TO_VECTOR('[1.2, 3.4]')) returns 2 without issue. Misinterpreting this could lead developers to over-constrain data prematurely-B's type mismatch is the clear error case, rooted in Oracle's vector type system.

NEW QUESTION # 57
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?

A. Exact similarity search using a full table scan
B. Approximate similarity search with IVF indexing and target accuracy of 70%
C. Approximate similarity search with HNSW indexing and target accuracy of 70%
D. Multivector similarity search with partitioning

Answer: A

Explanation:
To prioritize accuracy over speed, exact similarity search with a full table scan (C) computes distances between the query vector and all stored vectors, guaranteeing 100% recall without approximation trade-offs. HNSW with 70% target accuracy (A) and IVF with 70% (D) are approximate methods, sacrificing accuracy for speed via indexing (e.g., probing fewer neighbors). Multivector search (B) isn't a standard Oracle 23ai term; partitioning aids scale, not accuracy. Exact search, though slower, ensures maximum accuracy, as per Oracle's vector search options.

NEW QUESTION # 58
Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?

A. VECTOR_EMBEDDING
B. CREATE_VECTOR_EMBEDDING
C. GENERATE_EMBEDDING
D. EMBED_TEXT

Answer: A

Explanation:
The VECTOR_EMBEDDING function in Oracle Database 23ai generates a vector embedding from input data (e.g., a text string) using a specified model, such as an ONNX model loaded into the database. It's designed for in-database embedding creation, supporting vector search and AI applications. Options A, B, and C (GENERATE_EMBEDDING, CREATE_VECTOR_EMBEDDING, EMBED_TEXT) are not valid SQL functions in 23ai. VECTOR_EMBEDDING integrates seamlessly with the VECTOR data type and is documented as the standard method for embedding generation in SQL queries.

NEW QUESTION # 59
Which vector index available in Oracle Database 23ai is known for its speed and accuracy, making it a preferred choice for vector search?

A. Binary Tree (BT) index
B. Inverted File System (IFS) index
C. Hierarchical Navigable Small World (HNSW) index
D. Inverted File (IVF) index

Answer: C

Explanation:
Oracle 23ai supports two main vector indexes: IVF and HNSW. HNSW (D) is renowned for its speed and accuracy, using a hierarchical graph to connect vectors, enabling fast ANN searches with high recall-ideal for latency-sensitive applications like real-time RAG. IVF (C) partitions vectors for scalability but often requires tuning (e.g., NEIGHBOR_PARTITIONS) to match HNSW's accuracy, trading off recall for memory efficiency. BT (A) isn't a 23ai vector index; it's a generic term unrelated here. IFS (B) seems a typo for IVF; no such index exists. HNSW's graph structure outperforms IVF in small-to-medium datasets or where precision matters, as Oracle's documentation and benchmarks highlight, making it a go-to for balanced performance.

NEW QUESTION # 60
What is the primary purpose of the DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS package in a RAG application?

A. To generate vector embeddings from a text document
B. To load a document into the database
C. To convert a document into a single, large text string
D. To split a large document into smaller chunks to improve vector quality by minimizing token truncation

Answer: D

Explanation:
In Oracle Database 23ai, the DBMS_VECTOR_CHAIN package supports Retrieval Augmented Generation (RAG) workflows by providing utilities for vector processing. The UTL_TO_CHUNKS function specifically splits large documents into smaller, manageable text chunks. This is critical in RAG applications because embedding models (e.g., BERT, ONNX models) have token limits (e.g., 512 tokens). Splitting text minimizes token truncation, ensuring that each chunk retains full semantic meaning, which improves the quality of subsequent vector embeddings and search accuracy. Generating embeddings (A) is handled by functions like VECTOR_EMBEDDING, not UTL_TO_CHUNKS. Loading documents (B) is a separate process (e.g., via SQL*Loader). Converting to a single text string (D) contradicts the chunking purpose and risks truncation. Oracle's documentation on DBMS_VECTOR_CHAIN emphasizes chunking for optimizing vector quality in RAG.

NEW QUESTION # 61
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