2025 1Z0-184-25: ORACLE AI VECTOR SEARCH PROFESSIONAL UPDATED EXAM MATERIALS

2025 1Z0-184-25: Oracle AI Vector Search Professional Updated Exam Materials

2025 1Z0-184-25: Oracle AI Vector Search Professional Updated Exam Materials

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Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
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 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 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
  • 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
  • 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.

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Oracle AI Vector Search Professional Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which of the following actions will result in an error when using VECTOR_DIMENSION_COUNT() in Oracle Database 23ai?

  • A. Providing a vector with a dimensionality that exceeds the specified dimension count
  • B. Using a vector with a data type that is not supported by the function
  • 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: B

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 # 14
What is the purpose of the Vector Pool in Oracle Database 23ai?

  • A. To store HNSW vector indexes and IVF index metadata
  • B. To manage database partitioning
  • C. To enable longer SQL execution
  • D. To store non-vector data types

Answer: A

Explanation:
The Vector Pool in Oracle 23ai is a dedicated SGA memory region (controlled by VECTOR_MEMORY_SIZE) for vector operations, specifically storing HNSW indexes (graph structures) and IVF index metadata (e.g., centroids) (B). This optimizes memory usage for vector search, keeping critical index data accessible for fast queries. Partitioning (A) is unrelated; that's a tablespace feature. Longer SQL execution (C) might benefit indirectly from memory efficiency, but it's not the purpose. Non-vector data (D) resides elsewhere (e.g., PGA, buffer cache). Oracle allocates the Vector Pool to enhance AI workloads, ensuring indexes don't compete with other memory, a design choice reflecting vector search's growing importance.


NEW QUESTION # 15
What is the primary purpose of a similarity search in Oracle Database 23ai?

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

Answer: D

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 # 16
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 Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output
  • D. Load ONNX Model, Vectorize Question, 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 # 17
You need to generate a vector from the string '[1.2, 3.4]' in FLOAT32 format with 2 dimensions. Which function will you use?

  • A. FROM_VECTOR
  • B. TO_VECTOR
  • C. VECTOR_SERIALIZE
  • D. VECTOR_DISTANCE

Answer: B

Explanation:
In Oracle Database 23ai, the TO_VECTOR function (A) converts a string representation of a vector (e.g., '[1.2, 3.4]') into a VECTOR data type with specified format (e.g., FLOAT32) and dimensions (here, 2). It's designed for creating vectors from text input, matching the requirement. VECTOR_DISTANCE (B) calculates distances between vectors, not generates them.FROM_VECTOR (C) isn't a documented function; it might be confused with serialization or extraction, but it's not standard. VECTOR_SERIALIZE (D) converts a vector to a string, the opposite of what's needed. Oracle's SQL reference confirms TO_VECTOR for this purpose, parsing the string into a 2D FLOAT32 vector.


NEW QUESTION # 18
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