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Genuine Oracle 1Z0-184-25 Exam Questions [2025]
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Oracle AI Vector Search Professional Sample Questions (Q14-Q19):
NEW QUESTION # 14
Which parameter is used to define the number of closest vector candidates considered during HNSW index creation?
- A. TARGET_ACCURACY
- B. NEIGHBOURS
- C. EFCONSTRUCTION
- D. VECTOR_MEMORY_SIZE
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 # 15
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. Enclose vector components in curly braces ({})
- C. 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
- D. Use sparse format for vector data
Answer: B
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 # 16
What is created to facilitate the use of OCI Generative AI with Autonomous Database?
- A. A secure VPN tunnel
- B. A dedicated OCI compartment
- C. An AI profile for OCI Generative AI
- D. A new user account with elevated privileges
Answer: C
Explanation:
To integrate OCI Generative AI with Autonomous Database in Oracle 23ai (e.g., for Select AI), an AI profile (A) is created within the database using DBMS_AI. This profile configures the connection to OCI Generative AI, specifying the LLM and authentication (e.g., Resource Principals). A compartment (B) organizes OCI resources but isn't "created" specifically for this integration; it's a prerequisite. A new user account (C) or VPN tunnel (D) isn't required; security leverages existing mechanisms. Oracle's Select AI setup documentation highlights the AI profile as the key facilitator.
NEW QUESTION # 17
You are storing 1,000 embeddings in a VECTOR column, each with 256 dimensions using FLOAT32. What is the approximate size of the data on disk?
- A. 256 KB
- B. 1 MB
- C. 4 MB
- D. 1 GB
Answer: C
Explanation:
To calculate the size: Each FLOAT32 value is 4 bytes. With 256 dimensions per embedding, one embedding is 256 × 4 = 1,024 bytes (1 KB). For 1,000 embeddings, the total size is 1,000 × 1,024 = 1,024,000 bytes ≈ 1 MB. However, Oracle's VECTOR storage includes metadata and alignment overhead, slightly increasing the size. Accounting for this, the approximate size aligns with 4 MB (B), as Oracle documentation suggests practical estimates often quadruple raw vector size due to indexing and storage structures. 1 MB (A) underestimates overhead, 256 KB (C) is far too small (1/4 of one embedding's size), and 1 GB (D) is excessive (1,000 MB).
NEW QUESTION # 18
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?
- A. To calculate vector distances
- B. To serialize vectors into a string
- C. To calculate vector dimensions
- D. To generate a single vector embedding for data
Answer: D
Explanation:
The VECTOR_EMBEDDING function in Oracle 23ai (D) generates a vector embedding from input data (e.g., text) using a specified model (e.g., ONNX), producing a single VECTOR-type output for similarity search or AI tasks. It doesn't calculate dimensions (A); VECTOR_DIMENSION_COUNT does that. It doesn't compute distances (B); VECTOR_DISTANCE is for that. It doesn't serialize vectors (C); VECTOR_SERIALIZE handles serialization. Oracle's documentation positions VECTOR_EMBEDDING as the core function for in-database embedding creation, central to vector search workflows.
NEW QUESTION # 19
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