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Oracle 1Z0-184-25 認定試験の出題範囲：
トピック 出題範囲
トピック 1 Using Vector Embeddings: This section measures the abilities of AI Developers in generating and storing vector embeddings for AI applications. It covers generating embeddings both inside and outside the Oracle database and effectively storing them within the database for efficient retrieval and processing.

トピック 2 Building a RAG Application: This section assesses the knowledge of AI Solutions Architects in implementing retrieval-augmented generation (RAG) applications. Candidates will learn to build RAG applications using PL
SQL and Python to integrate AI models with retrieval techniques for enhanced AI-driven decision-making.

トピック 3 Performing Similarity Search: This section tests the skills of Machine Learning Engineers in conducting similarity searches to find relevant data points. It includes performing exact and approximate similarity searches using vector indexes. Candidates will also work with multi-vector similarity search to handle searches across multiple documents for improved retrieval accuracy.

トピック 4 Using Vector Indexes: This section evaluates the expertise of AI Database Specialists in optimizing vector searches using indexing techniques. It covers the creation of vector indexes to enhance search speed, including the use of HNSW and IVF vector indexes for performing efficient search queries in AI-driven applications.



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Oracle AI Vector Search Professional 認定 1Z0-184-25 試験問題 (Q47-Q52):
質問 # 47
You are tasked with finding the closest matching sentences across books, where each book has multiple paragraphs and sentences. Which SQL structure should you use?

A. GROUP BY with vector operations
B. A nested query with ORDER BY
C. FETCH PARTITIONS BY clause
D. Exact similarity search with a single query vector
正解：B

解説：
Finding the closest matching sentences across books involves comparing a query vector to sentence vectors stored in a table (e.g., columns: book_id, sentence, vector). A nested query with ORDER BY (A) is the optimal SQL structure: an inner query computes distances (e.g., SELECT sentence, VECTOR_DISTANCE(vector, :query_vector, COSINE) AS score FROM sentences), and the outer query sorts and limits results (e.g., SELECT * FROM (inner_query) ORDER BY score FETCH FIRST 5 ROWS ONLY). This ranks sentences by similarity, leveraging Oracle's vector capabilities efficiently, especially with an index.
Option B (exact search) describes a technique, not a structure, and a full scan is slow without indexing-lacking specificity here. Option C (GROUP BY) aggregates (e.g., by book), not ranks individual sentences, missing the "closest" goal. Option D (FETCH PARTITIONS BY) isn't a valid clause; it might confuse with IVF partitioning, but that's index-related, not query syntax. The nested structure allows flexibility (e.g., adding WHERE clauses) and aligns with Oracle's vector search examples, ensuring both correctness and scalability-crucial when books yield thousands of sentences.



質問 # 48
What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?

A. It is simpler and faster because it avoids square-root calculations
B. It is the default distance metric for Oracle AI Vector Search
C. It guarantees higher accuracy than Euclidean Distance
D. It supports hierarchical partitioning of vectors
正解：A

解説：
Euclidean Squared Distance (L2-squared) skips the square-root step of Euclidean Distance (L2), i.e., ∑(xi - yi)² vs. √∑(xi - yi)². Since the square root is monotonic, ranking order remains identical, but avoiding it (C) reduces computational cost, making queries faster-crucial for large-scale vector search. It's not the default metric (A); cosine is often default in Oracle 23ai. It doesn't relate to partitioning (B), an indexing feature. Accuracy (D) is equivalent, as rankings are preserved. Oracle's documentation notes L2-squared as an optimization for performance.



質問 # 49
What is the significance of splitting text into chunks in the process of loading data into Oracle AI Vector Search?

A. To minimize token truncation as each vector embedding model has its own maximum token limit
B. To reduce the computational burden on the embedding model
C. To facilitate parallel processing of the data during vectorization
正解：A

解説：
Splitting text into chunks (C) in Oracle AI Vector Search (e.g., via DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS) ensures that each segment fits within the token limit of embedding models (e.g., 512 tokens for BERT), preventing truncation that loses semantic content. This improves vector quality for similarity search. Reducing computational burden (A) is a secondary effect, not the primary goal. Parallel processing (B) may occur but isn't the main purpose; chunking is about model compatibility. Oracle's documentation emphasizes chunking to align with embedding model constraints.



質問 # 50
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. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy
C. Re-create the index with a higher EFCONSTRUCTION value
D. Increase the VECTOR_MEMORY_SIZE initialization parameter
正解：B

解説：
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.



質問 # 51
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. Converts the question to keywords, searches for matches, and inserts the text into the response
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
正解：D

解説：
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.



質問 # 52
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