Free PDF Quiz 2025 1Z0-184-25: High Pass-Rate Latest Oracle AI Vector Search Professional Exam Forum

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Oracle AI Vector Search Professional Sample Questions (Q29-Q34):

NEW QUESTION # 29
You are asked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?

A. Apply relational filters and a similarity search in the query
B. Use VECTOR_INDEX_HINT and NO WHERE clause
C. Perform the similarity search without a WHERE clause
D. Use UNION ALL with vector operations

Answer: A

Explanation:
To fetch the top five nearest vectors for a specific category, combine relational filtering (e.g., WHERE category = 'X') with similarity search (C) (e.g., VECTOR_DISTANCE with ORDER BY and FETCH FIRST 5 ROWS). UNION ALL (A) is for combining result sets, not filtering. Omitting WHERE (B) ignores the category constraint. VECTOR_INDEX_HINT (D) influences index usage, not filtering, and skipping WHERE misses the requirement. Oracle's vector search examples use WHERE clauses with similarity functions for such tasks.

NEW QUESTION # 30
What is the primary function of AI Smart Scan in Exadata System Software 24ai?

A. To provide real-time monitoring and diagnostics for AI applications
B. To accelerate AI workloads by leveraging Exadata RDMA Memory (XRMEM), Exadata Smart Cache, and on-storage processing
C. To automatically optimize database queries for improved performance

Answer: B

Explanation:
AI Smart Scan in Exadata System Software 24ai (B) accelerates AI workloads, including vector search, by offloading processing to storage servers using Exadata's RDMA Memory (XRMEM), Smart Cache, and on-storage capabilities. This enhances performance for large-scale vector operations. Real-time monitoring (A) isn't its focus; that's for management tools. Queryoptimization (C) is a general Exadata feature (Smart Scan), but AI Smart Scan specifically targets AI tasks. Oracle's 24ai documentation emphasizes its role in speeding up AI computations.

NEW QUESTION # 31
What are the key advantages and considerations of using Retrieval Augmented Generation (RAG) in the context of Oracle AI Vector Search?

A. It focuses on training specialized LLMs within the database environment for specific tasks, offering greater control over model behavior and data privacy but potentially requiring more development effort
B. It prioritizes real-time data extraction and summarization from various sources to ensure the LLM always has the most up-to-date information
C. It excels at optimizing the performance and efficiency of LLM inference through advanced caching and precomputation techniques, leading to faster response times but potentially increasing storage requirements
D. It leverages existing database security and access controls, thereby enabling secure and controlled access to both the database content and the LLM

Answer: D

Explanation:
RAG in Oracle AI Vector Search integrates vector search with LLMs, leveraging database-stored data. A key advantage is its use of existing database security and access controls (D), ensuring that sensitive enterprise data remains secure while being accessible to LLMs, aligning with Oracle's security model (e.g., roles, privileges). Performance optimization (A) occurs but isn't the primary focus; storage increases are minimal compared to security benefits. Real-time extraction (B) is possible but not RAG's core strength, which lies in static data augmentation. Training LLMs (C) is unrelated to RAG, which uses pre-trained models. Oracle emphasizes security integration as a standout RAG feature.

NEW QUESTION # 32
Which SQL statement correctly adds a VECTOR column named "v" with 4 dimensions and FLOAT32 format to an existing table named "my_table"?

A. UPDATE my_table SET v = VECTOR(4, FLOAT32)
B. ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32))
C. ALTER TABLE my_table ADD v VECTOR(4, FLOAT32)
D. ALTER TABLE my_table MODIFY (v VECTOR(4, FLOAT32))

Answer: B

Explanation:
To add a new column to an existing table, Oracle uses the ALTER TABLE statement with the ADD clause. Option B, ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32)), correctly specifies the column name "v", the VECTOR type, and its attributes (4 dimensions, FLOAT32 precision) within parentheses, aligning with Oracle's DDL syntax for VECTOR columns. Option A uses MODIFY, which alters existing columns, not adds new ones, making it incorrect here. Option C uses UPDATE, a DML statement for updating data, not a DDL operation for schema changes. Option D omits parentheses around the VECTOR specification, which is syntactically invalid as Oracle requires dimensions and format to be enclosed. The SQL Language Reference confirms this syntax for adding VECTOR columns.

NEW QUESTION # 33
Why would you choose to NOT define a specific size for the VECTOR column during development?

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

Answer: D

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 # 34
......

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