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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 4

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 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.

Oracle AI Vector Search Professional Sample Questions (Q33-Q38):

NEW QUESTION # 33
What is the first step in setting up the practice environment for Select AI?

A. Create a new user account with elevated privileges
B. Drop any compartment that does not use OCI Generative AI
C. Create a policy to enable access to OCI Generative AI
D. Optionally create an OCI compartment

Answer: D

Explanation:
Select AI in Oracle Database 23ai enables natural language queries by integrating with OCI Generative AI services. The first step in setting up the practice environment is to optionally create an OCI compartment (A), which organizes and isolates resources in Oracle Cloud Infrastructure (OCI). This is foundational because subsequent steps-like defining policies or configuring the Autonomous Database-depend on a compartment structure, though an existing compartment can be reused, making it optional. Creating a policy (B) is a subsequent step to grant access to OCIGenerative AI, requiring a compartment first. Dropping compartments (C) is irrelevant and disruptive. Creating a user account (D) is not specified as the initial step in Select AI setup. Oracle's Select AI documentation lists compartment setup as the starting point in OCI configuration.

NEW QUESTION # 34
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 HNSW indexing and target accuracy of 70%
B. Approximate similarity search with IVF indexing and target accuracy of 70%
C. Multivector similarity search with partitioning
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 # 35
Which function should you use to determine the storage format of a vector?

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

Answer: B

NEW QUESTION # 36
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

A. To calculate vector distances
B. To generate a single vector embedding for data
C. To serialize vectors into a string
D. To calculate vector dimensions

Answer: B

NEW QUESTION # 37
When generating vector embeddings outside the database, what is the most suitable option for storing the embeddings for later use?

A. In the database as BLOB (Binary Large Object) data
B. In a binary FVEC file with the relational data in a CSV file
C. In a dedicated vector database
D. In a CSV file

Answer: C

Explanation:
When vector embeddings are generated outside the database, the storage choice must balance efficiency, scalability, and usability for similarity search. A CSV file (A) is simple and human-readable but inefficient for large-scale vector operations due to text parsing overhead and lack of indexing support. A binary FVEC file (B) offers a compact format for vectors, reducing storage size and improving read performance, but separating relational data into a CSV complicates integration and querying, making it suboptimal for unified workflows. Storing embeddings as BLOBs in a relational database (C) integrates well with structured data and supports SQL access, but it lacks the specialized indexing (e.g., HNSW, IVF) and query optimizations that dedicated vector databases provide. A dedicated vector database (D), such as Milvus or Pinecone (or Oracle 23ai's vector capabilities if internal), is purpose-built for high-dimensional vectors, offering efficient storage, advanced indexing, and fast approximate nearest neighbor (ANN) searches. For external generation scenarios, where embeddings are not immediately integrated into Oracle 23ai, a dedicated vector database is the most suitable due to its performance and scalability advantages. Oracle's AI Vector Search documentation indirectly supports this by emphasizing optimized vector storage for search efficiency, though it focuses on in-database solutions.

NEW QUESTION # 38
......

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