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熱門的1Z0-184-25考試大綱，覆蓋大量的Oracle認證1Z0-184-25考試知識點

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Oracle 1Z0-184-25 考試大綱：

主題
簡介

主題 1

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.

主題 2

Understand Vector Fundamentals: This section of the exam measures the skills of Data Engineers in working with vector data types for storing embeddings and enabling semantic queries. It covers vector distance functions and metrics used in AI vector search. Candidates must demonstrate proficiency in performing DML and DDL operations on vectors to manage data efficiently.

主題 3

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.

最新的 Oracle Database 23ai 1Z0-184-25 免費考試真題 (Q25-Q30):

問題 #25
Which vector index available in Oracle Database 23ai is known for its speed and accuracy, making it a preferred choice for vector search?

A. Inverted File (IVF) index
B. Binary Tree (BT) index
C. Inverted File System (IFS) index
D. Hierarchical Navigable Small World (HNSW) index

答案：D

解題說明：
Oracle 23ai supports two main vector indexes: IVF and HNSW. HNSW (D) is renowned for its speed and accuracy, using a hierarchical graph to connect vectors, enabling fast ANN searches with high recall-ideal for latency-sensitive applications like real-time RAG. IVF (C) partitions vectors for scalability but often requires tuning (e.g., NEIGHBOR_PARTITIONS) to match HNSW's accuracy, trading off recall for memory efficiency. BT (A) isn't a 23ai vector index; it's a generic term unrelated here. IFS (B) seems a typo for IVF; no such index exists. HNSW's graph structure outperforms IVF in small-to-medium datasets or where precision matters, as Oracle's documentation and benchmarks highlight, making it a go-to for balanced performance.

問題 #26
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

A. To serialize vectors into a string
B. To calculate vector dimensions
C. To calculate vector distances
D. To generate a single vector embedding for data

答案：D

解題說明：
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.

問題 #27
In Oracle Database 23ai, which data type is used to store vector embeddings for similarity search?

A. VECTOR
B. VARCHAR2
C. BLOB
D. VECTOR2

答案：A

解題說明：
Oracle Database 23ai introduces the VECTOR data type (C) specifically for storing vector embeddings used in similarity search, supporting dimensions and formats (e.g., FLOAT32, INT8). VECTOR2 (A) doesn't exist. BLOB (B) can store binary data, including vectors, but lacks the semantic structure and indexing support of VECTOR. VARCHAR2 (D) is for text, not numerical arrays. VECTOR is optimized for AI vector search with native indexing (e.g., HNSW, IVF), as per Oracle's documentation.

問題 #28
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. Increase the VECTOR_MEMORY_SIZE initialization parameter
D. Re-create the index with a higher EFCONSTRUCTION value

答案：B

解題說明：
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.

問題 #29
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?

A. OCI Vision
B. OCI Data Science
C. OCI Generative AI
D. OCI Language

答案：C

解題說明：
Select AI in Oracle Database 23ai integrates with OCI Generative AI (B) to process natural language queries and generate context-aware responses using large language models (LLMs). OCI Language (A) focuses on text analysis (e.g., sentiment, entity recognition), not generative tasks. OCI Vision (C) handles image processing, unrelated to Select AI's text-based functionality. OCI Data Science (D) supports model development, not direct integration with Select AI. Oracle's documentation explicitly names OCI Generative AI as the integrated service for Select AI's LLM capabilities.

問題 #30
......

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