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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

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 3

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.

Oracle AI Vector Search Professional Sample Questions (Q35-Q40):

NEW QUESTION # 35
What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?

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

Answer: B

NEW QUESTION # 36
Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?

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

Answer: C

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 # 37
Which function should you use to determine the storage format of a vector?

A. VECTOR_DIMENSION_FORMAT
B. VECTOR_EMBEDDING
C. VECTOR_CHUNKS
D. VECTOR_NORM

Answer: A

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

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

Answer: D

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

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

Answer: D

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 # 40
......

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