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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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	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.

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Oracle AI Vector Search Professional Sample Questions (Q38-Q43):

NEW QUESTION # 38
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=SGA
B. ALTER SYSTEM RESET VECTOR_MEMORY_SIZE
C. ALTER DATABASE SET VECTOR_MEMORY_SIZE=1G SCOPE=VECTOR
D. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH

Answer: D

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 # 39
Why would you choose to NOT define a specific size for the VECTOR column during development?

A. It restricts the database to a single embedding model
B. Different external embedding models produce vectors with varying dimensions and data types
C. It limits the length of text that can be vectorized
D. It impacts the accuracy of similarity searches

Answer: B

Explanation:
In Oracle Database 23ai, a VECTOR column can be defined with a specific size (e.g., VECTOR(512, FLOAT32)) or left unspecified (e.g., VECTOR). Not defining a size (D) provides flexibility during development because different embedding models (e.g., BERT, SentenceTransformer) generate vectors with varying dimensions (e.g., 768, 384) and data types (e.g., FLOAT32, INT8). This avoids locking the schema into one model, allowing experimentation. Accuracy (A) isn't directly impacted by size definition; it depends on the model and metric. A fixed size doesn't restrict the database to one model (B) but requires matching dimensions. Text length (C) affects tokenization, not vector dimensions. Oracle's documentation supports undefined VECTOR columns for flexibility in AI workflows.

NEW QUESTION # 40
What is the purpose of the Vector Pool in Oracle Database 23ai?

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

Answer: D

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 # 41
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. VECTOR_DISTANCE
B. VECTOR_SERIALIZE
C. FROM_VECTOR
D. TO_VECTOR

Answer: D

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 # 42
Which vector index available in Oracle Database 23ai is known for its speed and accuracy, making it a preferred choice for vector search?

A. Inverted File (IVF) index
B. Hierarchical Navigable Small World (HNSW) index
C. Inverted File System (IFS) index
D. Binary Tree (BT) index

Answer: B

Explanation:
Oracle 23ai supports two main vector indexes: IVF and HNSW. HNSW (D) is renowned for its speed and accuracy, using a hierarchical graph to connect vectors, enabling fast ANN searches with high recall-ideal for latency-sensitive applications like real-time RAG. IVF (C) partitions vectors for scalability but often requires tuning (e.g., NEIGHBOR_PARTITIONS) to match HNSW's accuracy, trading off recall for memory efficiency. BT (A) isn't a 23ai vector index; it's a generic term unrelated here. IFS (B) seems a typo for IVF; no such index exists. HNSW's graph structure outperforms IVF in small-to-medium datasets or where precision matters, as Oracle's documentation and benchmarks highlight, making it a go-to for balanced performance.

NEW QUESTION # 43
......

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1Z0-184-25 Reliable Test Questions | Valid Dumps 1Z0-184-25 Free

Oracle 1Z0-184-25 Exam Syllabus Topics:

Valid Dumps 1Z0-184-25 Free | Vce 1Z0-184-25 File

Oracle AI Vector Search Professional Sample Questions (Q38-Q43):

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