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Oracle AI Vector Search Professional Sample Questions (Q59-Q64):NEW QUESTION # 59
You want to quickly retrieve the top-10 matches for a query vector from a dataset of billions of vectors, prioritizing speed over exact accuracy. What is the best approach?
  • A. Exact similarity search using flat search
  • B. Exact similarity search with a high target accuracy setting
  • C. Relational filtering combined with an exact search
  • D. Approximate similarity search with a low target accuracy setting
Answer: D
Explanation:
For speed over accuracy with billions of vectors, approximate similarity search (ANN) with a low target accuracy setting (B) (e.g., 70%) uses indexes like HNSW or IVF, probing fewer vectors to return top-10 matches quickly. Exact flat search (A) scans all vectors, too slow for billions. Relational filtering with exact search (C) adds overhead without speed gains. Exact search with high accuracy (D) maximizes precision but sacrifices speed. Oracle's documentation recommends ANN for large-scale, speed-focused queries.

NEW QUESTION # 60
What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?
  • A. Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output
  • B. Load ONNX Model, Vectorize Question, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
  • C. Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, Perform Vector Search, Generate Output
  • D. Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
Answer: A
Explanation:
Building a RAG application in Oracle 23ai using PL/SQL follows a logical sequence: (1) Load Document (e.g., via SQL*Loader) into the database; (2) Split Text into Chunks (e.g., DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS) to manage token limits; (3) Load ONNX Model (e.g., via DBMS_VECTOR) for embedding generation; (4) Create Embeddings (e.g., UTL_TO_EMBEDDINGS) for the chunks; (5) Vectorize Question (using the same model) when a query is received; (6) Perform Vector Search (e.g., VECTOR_DISTANCE) to find relevant chunks; (7) Generate Output (e.g., via DBMS_AI with an LLM). Option B matches this flow. A starts with the model prematurely. C prioritizes the question incorrectly. D is close but loads the model too early. Oracle's RAG workflow documentation outlines this document-first approach.

NEW QUESTION # 61
In the following Python code, what is the significance of prepending the source filename to each text chunk before storing it in the vector database?
bash
CollapseWrapCopy
docs = [{"text": filename + "|" + section, "path": filename} for filename, sections in faqs.items() for section in sections]
# Sample the resulting data
docs[:2]
  • A. It improves the accuracy of the LLM by providing additional training data
  • B. It preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file
  • C. It speeds up the vectorization process by providing a unique identifier for each chunk
  • D. It helps differentiate between chunks from different files but has no impact on vectorization
Answer: B
Explanation:
Prepending the filename to each text chunk (e.g., filename + "|" + section) in the Python code (A) preserves contextual metadata, linking each chunk-and its resulting vector-to its source file. This aids retrieval in RAG applications by allowing the application to trace back to the original document, enhancing response context (e.g., "from Book1"). While it differentiates chunks (B), its impact goes beyond identification, affecting retrieval usability. It doesn't speed up vectorization (C); embedding models process text regardless of prefixes. It also doesn't train the LLM (D); it's metadata for retrieval, not training data. Oracle's RAG examples emphasize metadata preservation for context-aware responses.

NEW QUESTION # 62
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. Increase the VECTOR_MEMORY_SIZE initialization parameter
  • C. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy
  • D. Change the index type to HNSW for better accuracy
Answer: C
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 # 63
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. 1 GB
  • B. 256 KB
  • C. 1 MB
  • D. 4 MB
Answer: D
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 # 64
......
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