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Oracle AI Vector Search Professional Sample Questions (Q58-Q63):

NEW QUESTION # 58
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. The physical storage location of the vector data
B. Regularly updating vector embeddings to reflect changes in the source data
C. The specific distance algorithm employed for vector comparisons
D. Using the same embedding model for both vector creation and similarity search

Answer: D

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 # 59
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. Rely on SQL*Loader's automatic normalization of vector data
B. 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
C. Use sparse format for vector data
D. Enclose vector components in curly braces ({})

Answer: D

Explanation:
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.

NEW QUESTION # 60
How does an application use vector similarity search to retrieve relevant information from a database, and how is this information then integrated into the generation process?

A. Converts the question to keywords, searches for matches, and inserts the text into the response
B. Clusters similar text chunks and randomly selects one from the most relevant cluster
C. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM
D. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt

Answer: D

Explanation:
In Oracle 23ai's RAG framework, vector similarity search (A) encodes a user question and database chunks into vectors (e.g., via VECTOR_EMBEDDING), computes similarity (e.g., cosine via VECTOR_DISTANCE), and retrieves the most relevant chunks. These are then included in the LLM prompt, augmenting its response with context. Training a separate LLM (B) is not RAG; RAG uses existing models. Keyword search (C) is traditional, not vector-based, and less semantic. Clustering and random selection (D) lacks precision and isn't RAG's approach. Oracle's documentation describes this encode-search-augment process as RAG's core mechanism.

NEW QUESTION # 61
Which parameter is used to define the number of closest vector candidates considered during HNSW index creation?

A. NEIGHBOURS
B. VECTOR_MEMORY_SIZE
C. EFCONSTRUCTION
D. TARGET_ACCURACY

Answer: C

Explanation:
In Oracle 23ai, EFCONSTRUCTION (A) controls the number of closest vector candidates (edges) considered during HNSW index construction, affecting the graph's connectivity and search quality. Higher values improve accuracy but increase build time. VECTOR_MEMORY_SIZE (B) sets memory allocation, not candidate count. NEIGHBOURS (C) isn't a parameter; it might confuse with NEIGHBOR_PARTITIONS (IVF). TARGET_ACCURACY (D) adjusts query-time accuracy, not index creation. Oracle's HNSW documentation specifies EFCONSTRUCTION for this purpose.

NEW QUESTION # 62
How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?

A. By requiring users to manually enter their OCI API keys each time they execute a natural language query
B. By encrypting all communication between the Autonomous Database and OCI Generative AI using TLS/SSL protocols
C. By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service
D. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials

Answer: D

Explanation:
In Oracle Database 23ai's Select AI, security between the Autonomous Database and OCI Generative AI is managed using Resource Principals (B). This mechanism allows the database instance to authenticate itself to OCI services without hardcoding credentials, enhancing security by avoiding exposure of sensitive keys. TLS/SSL encryption (A) is used for data-in-transit security, but it's a complementary layer, not the primary management method. A VPN tunnel (C) is unnecessary within OCI's secure infrastructure and not specified for Select AI. Manual API key entry (D) is impractical and insecure for automated database interactions. Oracle's documentation on Select AI highlights Resource Principals as the secure, scalable authentication method.

NEW QUESTION # 63
......

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1Z0-184-25 Test Simulator Online, 1Z0-184-25 Valid Exam Vce

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Oracle AI Vector Search Professional Sample Questions (Q58-Q63):

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