PASS GUARANTEED 2025 1Z0-184-25: TRUSTABLE ORACLE AI VECTOR SEARCH PROFESSIONAL TRAINING MATERIAL

Pass Guaranteed 2025 1Z0-184-25: Trustable Oracle AI Vector Search Professional Training Material

Pass Guaranteed 2025 1Z0-184-25: Trustable Oracle AI Vector Search Professional Training Material

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Oracle AI Vector Search Professional Sample Questions (Q14-Q19):

NEW QUESTION # 14
A machine learning team is using IVF indexes in Oracle Database 23ai to find similar images in a large dataset. During testing, they observe that the search results are often incomplete, missing relevant images. They suspect the issue lies in the number of partitions probed. How should they improve the search accuracy?

  • A. Change the index type to HNSW for better accuracy
  • B. Re-create the index with a higher EFCONSTRUCTION value
  • C. Increase the VECTOR_MEMORY_SIZE initialization parameter
  • D. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy

Answer: D

Explanation:
IVF (Inverted File) indexes in Oracle 23ai partition vectors into clusters, probing a subset during queries for efficiency. Incomplete results suggest insufficient partitions are probed, reducing recall. The TARGET_ACCURACY clause (A) allows users to specify a desired accuracy percentage (e.g., 90%), dynamically increasing the number of probed partitions to meet this target, thus improving accuracy at the cost of latency. Switching to HNSW (B) offers higher accuracy but requires re-indexing and may not be necessary if IVF tuning suffices. Increasing VECTOR_MEMORY_SIZE (C) allocates more memory for vector operations but doesn't directly affect probe count. EFCONSTRUCTION (D) is an HNSW parameter, irrelevant to IVF. Oracle's IVF documentation highlights TARGET_ACCURACY as the recommended tuning mechanism.


NEW QUESTION # 15
What is the primary purpose of a similarity search in Oracle Database 23ai?

  • A. Optimize relational database operations to compute distances between all data points in a database
  • B. To retrieve the most semantically similar entries using distance metrics between different vectors
  • C. To find exact matches in BLOB data
  • D. To group vectors by their exact scores

Answer: B

Explanation:
Similarity search in Oracle 23ai (C) uses vector embeddings in VECTOR columns to retrieve entries semantically similar to a query vector, based on distance metrics (e.g., cosine, Euclidean) via functions like VECTOR_DISTANCE. This is key for AI applications like RAG, finding "close" rather than exact matches. Optimizing relational operations (A) is unrelated; similarity search is vector-specific. Exact matches in BLOBs (B) don't leverage vector semantics. Grouping by scores (D) is a post-processing step, not the primary purpose. Oracle's documentation defines similarity search as retrieving semantically proximate vectors.


NEW QUESTION # 16
Which parameter is used to define the number of closest vector candidates considered during HNSW index creation?

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

Answer: D

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 # 17
What is the default distance metric used by the VECTOR_DISTANCE function if none is specified?

  • A. Manhattan
  • B. Hamming
  • C. Cosine
  • D. Euclidean

Answer: C

Explanation:
The VECTOR_DISTANCE function in Oracle 23ai computes vector distances, and if no metric is specified (e.g., VECTOR_DISTANCE(v1, v2)), it defaults to Cosine (C). Cosine distance (1 - cosine similarity) is widely used for text embeddings due to its focus on angular separation, ignoring magnitude-fitting for normalized vectors from models like BERT. Euclidean (A) measures straight-line distance, not default. Hamming (B) is for binary vectors, rare in 23ai's FLOAT32 context. Manhattan (D) sums absolute differences, less common for embeddings. Oracle's choice of Cosine reflects its AI focus, as documentation confirms, aligning with industry norms for semantic similarity-vital for users assuming defaults in queries.


NEW QUESTION # 18
Which function should you use to determine the storage format of a vector?

  • A. VECTOR_CHUNKS
  • B. VECTOR_DIMENSION_FORMAT
  • C. VECTOR_NORM
  • D. VECTOR_EMBEDDING

Answer: B


NEW QUESTION # 19
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