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1Z0-184-25 Übungsfragen: Oracle AI Vector Search Professional & 1Z0-184-25 Dateien Prüfungsunterlagen

Wenn Sie die neuesten und genauesten Prüfungsfragen zur Oracle 1Z0-184-25 Zertifizierungsprüfung von ZertSoft wählen, ist der Erfolg nicht weit entfernt.

Oracle 1Z0-184-25 Prüfungsplan:

Thema
Einzelheiten

Thema 1

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.

Thema 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.

Thema 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.

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1Z0-184-25 Vorbereitung & 1Z0-184-25 Schulungsangebot

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Oracle AI Vector Search Professional 1Z0-184-25 Prüfungsfragen mit Lösungen (Q21-Q26):

21. Frage
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?

A. OCI Data Science
B. OCI Vision
C. OCI Language
D. OCI Generative AI

Antwort: D

Begründung:
Select AI in Oracle Database 23ai integrates with OCI Generative AI (B) to process natural language queries and generate context-aware responses using large language models (LLMs). OCI Language (A) focuses on text analysis (e.g., sentiment, entity recognition), not generative tasks. OCI Vision (C) handles image processing, unrelated to Select AI's text-based functionality. OCI Data Science (D) supports model development, not direct integration with Select AI. Oracle's documentation explicitly names OCI Generative AI as the integrated service for Select AI's LLM capabilities.

22. Frage
When generating vector embeddings outside the database, what is the most suitable option for storing the embeddings for later use?

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

Antwort: B

Begründung:
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.

23. Frage
When using SQL*Loader to load vector data for search applications, what is a critical consideration regarding the formatting of the vector data within the input CSV file?

A. Enclose vector components in curly braces ({})
B. Use sparse format for vector data
C. Rely on SQL*Loader's automatic normalization of vector data
D. As FVEC is a binary format and the vector dimensions have a known width, fixed offsets can be used to make parsing the vectors fast and efficient

Antwort: A

Begründung:
SQLLoader in Oracle 23ai supports loading VECTOR data from CSV files, requiring vectors to be formatted as text. A critical consideration is enclosing components in curly braces (A), e.g., {1.2, 3.4, 5.6}, to match the VECTOR type's expected syntax (parsed into FLOAT32, etc.). FVEC (B) is a binary format, not compatible with CSV text input; SQLLoader expects readable text, not fixed offsets. Sparse format (C) isn't supported for VECTOR columns, which require dense arrays. SQLLoader doesn't normalize vectors automatically (D); formatting must be explicit. Oracle's documentation specifies curly braces for CSV-loaded vectors.

24. Frage
What is the primary function of an embedding model in the context of vector search?

A. To store vectors in a structured format for efficient retrieval
B. To transform text or data into numerical vector representations
C. To execute similarity search operations within a database
D. To define the schema for a vector database

Antwort: B

Begründung:
An embedding model in the context of vector search, such as those used in Oracle Database 23ai, is fundamentally a machine learning construct (e.g., BERT, SentenceTransformer, or an ONNX model) designed to transform raw data-typically text, but also images or other modalities-into numerical vector representations (C). These vectors, stored in the VECTOR data type, encapsulate semantic meaning in a high-dimensional space where proximity reflects similarity. For instance, the word "cat" might be mapped to a 512-dimensional vector like [0.12, -0.34, ...], where its position relative to "dog" indicates relatedness. This transformation is the linchpin of vector search, enabling mathematical operations like cosine distance to find similar items.
Option A (defining schema) misattributes a database design role to the model; schema is set by DDL (e.g., CREATE TABLE with VECTOR). Option B (executing searches) confuses the model with database functions like VECTOR_DISTANCE, which use the embeddings, not create them. Option D (storing vectors) pertains to the database's storage engine, not the model's function-storage is handled by Oracle's VECTOR type and indexes (e.g., HNSW). The embedding model's role is purely generative, not operational or structural. In practice, Oracle 23ai integrates this via VECTOR_EMBEDDING, which calls the model to produce vectors, underscoring its transformative purpose. Misunderstanding this could lead to conflating data preparation with query execution, a common pitfall for beginners.

25. Frage
What is the primary purpose of a similarity search in Oracle Database 23ai?

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

Antwort: B

Begründung:
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.

26. Frage
......

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