通過這幾年IT行業不斷的發展與壯大，1z0-1195-26考試已經成為Oracle考試裏的里程碑，可以讓你成為IT的專業人士，有數以百計的線上資源，提供Oracle的1z0-1195-26考試的問題，為什麼大多數選擇Testpdf，因為我們Testpdf裏有一支龐大的IT精英團隊，專注於Oracle的1z0-1195-26考試的最新資料。讓你無障礙通過Oracle的1z0-1195-26考試認證。Testpdf保證你第一次嘗試通過Oracle的1z0-1195-26考試取得認證，Testpdf會和你站在一起，與你同甘共苦。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Working with JSON and Graph in Oracle AI Database | 20% | - Distinguish when graph capabilities and Property Graph Views fit a business use case - Describe core graph concepts and graph analytic capabilities - Explain JSON and Oracle AI Database JSON capabilities |
| Topic 2: Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Determine how AI Vector Search supports GenAI pipelines and RAG - Describe Select AI in Autonomous AI Database - Apply AI Vector Search to combined semantic and business-data search scenarios |
| Topic 3: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Create an Autonomous AI Database Serverless instance for a basic workload - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Describe Autonomous AI Database characteristics, offerings, and deployment choices |
| Topic 4: Building Low-Code Applications and Agentic AI | 10% | - Describe Oracle APEX as Oracle's low-code platform - Choose the appropriate Agent Factory capability for a no-code AI agent use case |
| Topic 5: Working with AI and Vector Foundations | 15% | - Explain vectors, embeddings, and the Oracle VECTOR data type - Describe AI, AGI, and machine learning foundations - Apply vector distance and indexing concepts to similarity search needs |
| Topic 6: Using Oracle Database Actions and Data Studio Tools | 15% | - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks - Describe Database Actions and core development tools |
Testpdf的經驗豐富的專家團隊開發出了針對Oracle 1z0-1195-26 認證考試的有效的培訓計畫，很適合參加Oracle 1z0-1195-26 認證考試的考生。Testpdf為你提供的都是高品質的產品，可以讓你參加Oracle 1z0-1195-26 認證考試之前做模擬考試，可以為你參加考試做最好的準備。
問題 #48
Which sequence matches a simple RAG pipeline?
答案：B
解題說明：
A Retrieval-Augmented Generation pipeline depends on retrieval occurring before final response generation.
Source content is first processed into meaningful chunks. An embedding model converts those chunks into numerical vectors representing semantic meaning, and those vectors are stored in a vector store or indexed vector column. When a user submits a question, the question is also represented as an embedding. Similarity search then compares the query vector with stored vectors and retrieves the most semantically relevant chunks. Those retrieved chunks provide grounding context that is supplied to the LLM before it generates the final response.
Oracle AI Database supports this architecture through native vector storage, embedding generation, vector indexes, similarity functions, and Select AI RAG. Oracle specifically describes RAG as retrieving enterprise information through AI Vector Search and augmenting the prompt supplied to the LLM. Generating the response before retrieval defeats the fundamental purpose of RAG because the model would not yet have the grounding context. Likewise, graph modeling and workspace provisioning are not mandatory steps in the basic RAG pipeline. The question source identifies the embedding # storage # retrieval # generation sequence as correct.
Study Guide reference: Working with AI and Vector Foundations - embeddings, vector stores, semantic retrieval, and Retrieval-Augmented Generation.
問題 #49
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
答案：D
解題說明：
A vector index with approximate similarity search is designed specifically for high-performance top-K retrieval over large vector collections. Exact vector search calculates distances against all candidate vectors that satisfy the query predicates, which can become computationally expensive at scale. Approximate nearest- neighbor search uses vector indexing structures to reduce the number of candidate vectors evaluated, significantly improving search latency while accepting a controlled trade-off between performance and recall or accuracy. Oracle AI Database supports vector indexes with organizations such as INMEMORY NEIGHBOR GRAPH and NEIGHBOR PARTITIONS and allows administrators to configure target accuracy.
This requirement explicitly states that approximate top-K results are acceptable, making an approximate vector index the intended architecture. A conventional B-tree index is appropriate for scalar equality, ordering, or range-access patterns, not high-dimensional semantic similarity. JSON Duality Views provide document-relational mapping rather than nearest-neighbor acceleration. Property graph views model entities and relationships and likewise do not serve as vector similarity indexes. The uploaded assessment identifies "a vector index with approximate search" as the correct option.
Study Guide reference: Working with AI and Vector Foundations - vector indexes, approximate nearest- neighbor search, top-K retrieval, and target accuracy.
問題 #50
Which output can Select AI deliver to an application?
答案：B
解題說明：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Select AI supports multiple response modes depending on the action requested. With the default runsql action, Oracle generates SQL from the natural-language prompt, executes it, and returns the resulting data. The showsql action returns the generated SQL statement without executing it, while narrate executes the generated query and sends its results to the configured LLM to produce a natural-language description. Oracle additionally supports actions such as explainsql, chat, and summarize. Therefore, an application can receive a database result set, generated SQL, or a narrative response depending on how Select AI is invoked. Graph visualizations, patch-history maintenance recommendations, and automatic JSON export files are not Select AI output modes. This directly aligns with Oracle's Select AI actions and natural-language database interaction capabilities.
問題 #51
Which pair correctly matches an AI domain to an example?
答案：B
解題說明：
Vision - image classification is the correctly matched AI domain and use case. The uploaded source explicitly identifies option A as correct. Oracle Cloud Infrastructure Vision documentation confirms that Vision performs image analysis and includes image-classification capabilities for identifying objects and scene-based characteristics in images.
The distinction among the answer choices is based on the type of input being analyzed and the objective of the AI model. Computer vision works with images and visual content; classification assigns labels or categories based on visual characteristics. Language capabilities operate primarily on natural-language text and support functions such as entity recognition, sentiment analysis, text classification, and key-phrase extraction. Therefore, object detection in photographs belongs to vision rather than language.
Likewise, forecasting predicts future numerical or temporal outcomes from historical patterns; product- demand prediction is a typical forecasting scenario. Speech focuses on spoken audio, such as transcription or speech recognition, rather than business-demand prediction.
Option A is therefore the only domain/example relationship that is semantically and technically aligned.
Oracle Vision explicitly supports image classification, making the mapping unambiguous.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - AI domains, vision, language, speech, forecasting, and practical AI use cases.
問題 #52
What does a JSON Duality View enable?
答案：B
解題說明：
A JSON-Relational Duality View allows applications to work with relationally stored information as JSON documents without maintaining a separate document-store copy. The uploaded source identifies this exact capability as the correct answer. Oracle AI Database documentation confirms that a duality view maps relational table data to hierarchical JSON documents that are materialized on demand rather than separately stored. Applications can therefore access and, when permitted, modify the same underlying information either through relational tables or through its document representation.
This architecture preserves relational advantages such as normalization, integrity constraints, SQL processing, and transactional consistency while giving document-oriented applications a natural JSON interface. A change made through an updatable JSON document is reflected in the underlying relational data, and relational changes are correspondingly visible through the duality view.
The feature does not eliminate SQL, transform property graphs into vector indexes, or require synchronization with an independent document database. Oracle specifically positions JSON-Relational Duality as a mechanism for combining relational and document development models around one authoritative data representation.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - JSON-Relational Duality Views, relational storage, and document-oriented access.
問題 #53
......
如果你擁有了Testpdf Oracle的1z0-1195-26考試培訓資料，我們將免費為你提供一年的更新，這意味著你總是得到最新的考試認證資料，只要考試目標有所變化，以及我們的學習材料有所變化，我們將在第一時間為你更新。我們知道你的需求，我們將幫助得到 Oracle的1z0-1195-26考試認證的信心，讓你可以安然無憂的去參加考試，並順利通過獲得認證。
1z0-1195-26題庫更新: https://www.testpdf.net/1z0-1195-26.html