Quiz Oracle Pass-Sure 1Z0-184-25 - Oracle AI Vector Search Professional Dump Collection

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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
Topic 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.
Topic 2	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 3	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 4	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 (Q39-Q44):

NEW QUESTION # 39
What is a key characteristic of HNSW vector indexes?

A. They use hash-based clustering
B. They require exact match for searches
C. They are disk-based structures
D. They are hierarchical with multilayered connections

Answer: D

Explanation:
HNSW (Hierarchical Navigable Small World) indexes in Oracle 23ai (A) are characterized by a hierarchical structure with multilayered connections, enabling efficient approximate nearest neighbor (ANN) searches. This graph-based approach connects vectors across levels, balancing speed and accuracy. They don't require exact matches (B); they're designed for approximate searches. They're memory-optimized, not solely disk-based (C), though persisted to disk. Hash-based clustering (D) relates to other methods (e.g., LSH), not HNSW. Oracle's documentation highlights HNSW's hierarchical nature as key to its performance.

NEW QUESTION # 40
A database administrator wants to change the VECTOR_MEMORY_SIZE parameter for a pluggable database (PDB) in Oracle Database 23ai. Which SQL command is correct?

A. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH
B. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=SGA
C. ALTER DATABASE SET VECTOR_MEMORY_SIZE=1G SCOPE=VECTOR
D. ALTER SYSTEM RESET VECTOR_MEMORY_SIZE

Answer: A

Explanation:
VECTOR_MEMORY_SIZE in Oracle 23ai controls memory allocation for vector operations (e.g., indexing, search) in the SGA. For a PDB, ALTER SYSTEM adjusts parameters, andSCOPE=BOTH (A) applies the change immediately and persists it across restarts (modifying the SPFILE). Syntax: ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH sets it to 1 GB. Option B (ALTER DATABASE) is invalid for this parameter, and SCOPE=VECTOR isn't a valid scope. Option C (SCOPE=SGA) isn't a scope value; valid scopes are MEMORY, SPFILE, or BOTH. Option D (RESET) reverts to default, not sets a value. In a PDB, this must be executed in the PDB context, not CDB, and BOTH ensures durability-key for production environments where vector workloads demand consistent memory.

NEW QUESTION # 41
What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?

A. It is the default distance metric for Oracle AI Vector Search
B. It guarantees higher accuracy than Euclidean Distance
C. It supports hierarchical partitioning of vectors
D. It is simpler and faster because it avoids square-root calculations

Answer: D

Explanation:
Euclidean Squared Distance (L2-squared) skips the square-root step of Euclidean Distance (L2), i.e., ∑(xi - yi)² vs. √∑(xi - yi)². Since the square root is monotonic, ranking order remains identical, but avoiding it (C) reduces computational cost, making queries faster-crucial for large-scale vector search. It's not the default metric (A); cosine is often default in Oracle 23ai. It doesn't relate to partitioning (B), an indexing feature. Accuracy (D) is equivalent, as rankings are preserved. Oracle's documentation notes L2-squared as an optimization for performance.

NEW QUESTION # 42
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

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

Answer: A

NEW QUESTION # 43
Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?

A. Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications
B. Because of the complexity of vector data, Data Pump requires a specialized plug-in to handle the export and import operations involving vector data types
C. Data Pump treats vector embeddings as regular text strings, which can lead to data corruption or loss of precision when transferring vector data for vector search
D. Data Pump only exports and imports vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database

Answer: A

Explanation:
Oracle Data Pump in 23ai natively supports the VECTOR data type (C), allowing export and import of tables with vector columns without conversion or plug-ins. This facilitates vector search application migrations, preserving dimensional and format integrity (e.g., FLOAT32). BLOB storage (A) isn't required; VECTOR is a distinct type. Data Pump doesn't treat vectors as text (B), avoiding corruption; it handles them as structured arrays. No specialized plug-in (D) is needed; native support is built-in. Oracle's Data Pump documentation confirms seamless handling of VECTOR data.

NEW QUESTION # 44
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QUIZ ORACLE PASS-SURE 1Z0-184-25 - ORACLE AI VECTOR SEARCH PROFESSIONAL DUMP COLLECTION

Quiz Oracle Pass-Sure 1Z0-184-25 - Oracle AI Vector Search Professional Dump Collection