RELIABLE LATEST 1Z0-184-25 QUESTIONS & ACCURATE INSTANT 1Z0-184-25 DISCOUNT & EFFICIENT CERTIFICATION 1Z0-184-25 EXAM

Reliable Latest 1Z0-184-25 Questions & Accurate Instant 1Z0-184-25 Discount & Efficient Certification 1Z0-184-25 Exam

Reliable Latest 1Z0-184-25 Questions & Accurate Instant 1Z0-184-25 Discount & Efficient Certification 1Z0-184-25 Exam

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Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
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
  • 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 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.

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Oracle AI Vector Search Professional Sample Questions (Q16-Q21):

NEW QUESTION # 16
Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?

  • A. VECTOR_EMBEDDING
  • B. GENERATE_EMBEDDING
  • C. CREATE_VECTOR_EMBEDDING
  • D. EMBED_TEXT

Answer: A

Explanation:
The VECTOR_EMBEDDING function in Oracle Database 23ai generates a vector embedding from input data (e.g., a text string) using a specified model, such as an ONNX model loaded into the database. It's designed for in-database embedding creation, supporting vector search and AI applications. Options A, B, and C (GENERATE_EMBEDDING, CREATE_VECTOR_EMBEDDING, EMBED_TEXT) are not valid SQL functions in 23ai. VECTOR_EMBEDDING integrates seamlessly with the VECTOR data type and is documented as the standard method for embedding generation in SQL queries.


NEW QUESTION # 17
What security enhancement is introduced in Exadata System Software 24ai?

  • A. Enhanced encryption algorithm for data at rest
  • B. Integration with third-party security tools
  • C. SNMP security (Security Network Management Protocol)

Answer: A

Explanation:
Exadata System Software 24ai (noted in context beyond 23ai) introduces an enhanced encryption algorithm for data at rest (B), strengthening security for stored data, including vectors. Third-party integration (A) isn't highlighted as a 24ai feature. SNMP security (C) relates to network monitoring, not a primary Exadata enhancement. Oracle's Exadata documentation for 24ai emphasizes advanced encryption as a key security upgrade.


NEW QUESTION # 18
In Oracle Database 23ai, which data type is used to store vector embeddings for similarity search?

  • A. BLOB
  • B. VARCHAR2
  • C. VECTOR2
  • D. VECTOR

Answer: D

Explanation:
Oracle Database 23ai introduces the VECTOR data type (C) specifically for storing vector embeddings used in similarity search, supporting dimensions and formats (e.g., FLOAT32, INT8). VECTOR2 (A) doesn't exist. BLOB (B) can store binary data, including vectors, but lacks the semantic structure and indexing support of VECTOR. VARCHAR2 (D) is for text, not numerical arrays. VECTOR is optimized for AI vector search with native indexing (e.g., HNSW, IVF), as per Oracle's documentation.


NEW QUESTION # 19
You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?

  • A. Using the same embedding model for both vector creation and similarity search
  • B. Regularly updating vector embeddings to reflect changes in the source data
  • C. The specific distance algorithm employed for vector comparisons
  • D. The physical storage location of the vector data

Answer: A

Explanation:
In Oracle Database 23ai, vector search accuracy hinges on the consistency of the embedding model. The VECTOR data type stores embeddings as fixed-dimensional arrays, and similarity searches (e.g., using VECTOR_DISTANCE) assume that all vectors-stored and query-are generated by the same model. This ensures they occupy the same semantic space, making distance calculations meaningful. Regular updates (B) maintain data freshness, but if the model changes, integrity is compromised unless all embeddings are regenerated consistently. The distance algorithm (C) (e.g., cosine, Euclidean) defines how similarity is measured but relies on consistent embeddings; an incorrect model mismatch undermines any algorithm. Physical storage location (D) affects performance, not integrity. Oracle's documentation stresses model consistency as a prerequisite for reliable vector search within its native capabilities.


NEW QUESTION # 20
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 DATABASE SET VECTOR_MEMORY_SIZE=1G SCOPE=VECTOR
  • B. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=SGA
  • C. ALTER SYSTEM RESET VECTOR_MEMORY_SIZE
  • 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 # 21
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