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[General] 1Z0-184-25 Valid Vce Dumps | 1Z0-184-25 Reliable Braindumps Pdf

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【General】 1Z0-184-25 Valid Vce Dumps | 1Z0-184-25 Reliable Braindumps Pdf

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Oracle AI Vector Search Professional Sample Questions (Q59-Q64):NEW QUESTION # 59
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. Re-create the index with a higher EFCONSTRUCTION value
  • B. Change the index type to HNSW for better accuracy
  • 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 # 60
An application needs to fetch the top-3 matching sentences from a dataset of books while ensuring a balance between speed and accuracy. Which query structure should you use?
  • A. A combination of relational filters and similarity search
  • B. Exact similarity search with Euclidean distance
  • C. Approximate similarity search with the VECTOR_DISTANCE function
  • D. Multivector similarity search with approximate fetching and target accuracy
Answer: C
Explanation:
Fetching the top-3 matching sentences requires a similarity search, and balancing speed and accuracy points to approximate nearest neighbor (ANN) techniques. Option A-approximate similarity search with VECTOR_DISTANCE-uses an index (e.g., HNSW, IVF) to quickly find near-matches, ordered by distance (e.g., SELECT sentence, VECTOR_DISTANCE(vector, :query_vector, COSINE) AS score FROM books ORDER BY score FETCH APPROXIMATE 3 ROWS ONLY). The APPROXIMATE clause leverages indexing for speed, with tunable accuracy (e.g., TARGET_ACCURACY), ideal for large datasets where exactness is traded for performance.
Option B (exact search with Euclidean) scans all vectors without indexing, ensuring 100% accuracy but sacrificing speed-impractical for big datasets. Option C ("multivector" search) isn't a standard Oracle 23ai construct; it might imply multiple vectors per row, but lacks clarity and isn't optimal here. Option D (relational filters plus similarity) adds WHERE clauses (e.g., WHERE genre = 'fiction'), useful for scoping but not specified as needed, and doesn't inherently balance speed-accuracy without ANN. Oracle's ANN support in 23ai, via HNSW or IVF withVECTOR_DISTANCE, makes A the practical choice, aligning with real-world RAG use cases where response time matters as much as relevance.

NEW QUESTION # 61
You need to generate a vector from the string '[1.2, 3.4]' in FLOAT32 format with 2 dimensions. Which function will you use?
  • A. VECTOR_DISTANCE
  • B. VECTOR_SERIALIZE
  • C. FROM_VECTOR
  • D. TO_VECTOR
Answer: D
Explanation:
In Oracle Database 23ai, the TO_VECTOR function (A) converts a string representation of a vector (e.g., '[1.2, 3.4]') into a VECTOR data type with specified format (e.g., FLOAT32) and dimensions (here, 2). It's designed for creating vectors from text input, matching the requirement. VECTOR_DISTANCE (B) calculates distances between vectors, not generates them.FROM_VECTOR (C) isn't a documented function; it might be confused with serialization or extraction, but it's not standard. VECTOR_SERIALIZE (D) converts a vector to a string, the opposite of what's needed. Oracle's SQL reference confirms TO_VECTOR for this purpose, parsing the string into a 2D FLOAT32 vector.

NEW QUESTION # 62
What is a key characteristic of HNSW vector indexes?
  • A. They use hash-based clustering
  • B. They are disk-based structures
  • C. They require exact match for searches
  • D. They are hierarchical with multilayered connections
Answer: D
Explanation:
HNSW (Hierarchical Navigable Small World) indexes in Oracle 23ai (A) are characterized by a hierarchical structure with multilayered connections, enabling efficient approximate nearest neighbor (ANN) searches. This graph-based approach connects vectors across levels, balancing speed and accuracy. They don't require exact matches (B); they're designed for approximate searches. They're memory-optimized, not solely disk-based (C), though persisted to disk. Hash-based clustering (D) relates to other methods (e.g., LSH), not HNSW. Oracle's documentation highlights HNSW's hierarchical nature as key to its performance.

NEW QUESTION # 63
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. Clusters similar text chunks and randomly selects one from the most relevant cluster
  • B. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt
  • C. Converts the question to keywords, searches for matches, and inserts the text into the response
  • D. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM
Answer: B
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 # 64
......
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