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有效的Oracle 1Z0-184-25：Oracle AI Vector Search Professional認證指南 -熱門的Testpdf 1Z0-184-25考試資訊

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Oracle 1Z0-184-25 考試大綱：

主題
簡介

主題 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.

主題 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.

主題 3

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 1Z0-184-25考試資訊，1Z0-184-25題庫更新資訊

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最新的 Oracle Database 23ai 1Z0-184-25 免費考試真題 (Q17-Q22):

問題 #17
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

答案：D

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 #18
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

答案：D

解題說明：
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).

問題 #19
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

答案：A

解題說明：
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.

問題 #20
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_SERIALIZE
B. FROM_VECTOR
C. TO_VECTOR
D. VECTOR_DISTANCE

答案：C

解題說明：
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.

問題 #21
What is created to facilitate the use of OCI Generative AI with Autonomous Database?

A. A new user account with elevated privileges
B. A dedicated OCI compartment
C. A secure VPN tunnel
D. An AI profile for OCI Generative AI

答案：D

解題說明：
To integrate OCI Generative AI with Autonomous Database in Oracle 23ai (e.g., for Select AI), an AI profile (A) is created within the database using DBMS_AI. This profile configures the connection to OCI Generative AI, specifying the LLM and authentication (e.g., Resource Principals). A compartment (B) organizes OCI resources but isn't "created" specifically for this integration; it's a prerequisite. A new user account (C) or VPN tunnel (D) isn't required; security leverages existing mechanisms. Oracle's Select AI setup documentation highlights the AI profile as the key facilitator.

問題 #22
......

1Z0-184-25 考古題覆蓋了最新的考試指南，確保考生一次性通過 1Z0-184-25 考試。Oracle 認證專家根據 1Z0-184-25 考試主題編訂，適合全球的考生使用，提高考生的通過率。Oracle Database 23ai 是一張高級網路專家認可證書，亦是全球公認的專業認證。1Z0-184-25 認證主要的目的是讓網路工程師能在現今變化迅速的資訊網路環境中，都能掌握和擁有最先進的網路技術，任何時候都能保持領導地位。

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