誰もが良い仕事とまともな収入を望んでいます。 しかし、彼らが優れた能力と優れた主要な知識を持っていない場合、彼らはまともな仕事を見つけるのは難しいです。 Oracleテスト1z0-1195-26認定に合格すると、夢を実現し、満足のいく仕事を見つけることができます。 1z0-1195-26学習教材は、1z0-1195-26のOracle AI Database Foundations Associate試験に簡単に合格するのに役立つ優れたツールです。 時間をかけて学習する必要はありません。 1z0-1195-26試験ガイドは高品質であり、当社Oracleの製品を使用する場合、1z0-1195-26試験に合格する可能性は99％〜100％と非常に高くなっています。
| Section | Weight | Objectives |
|---|---|---|
| Building Low-Code Applications and Agentic AI | 10% | - Choose the appropriate Agent Factory capability for a no-code AI agent use case - Describe Oracle APEX as Oracle's low-code platform |
| 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 |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Describe Select AI in Autonomous AI Database - Apply AI Vector Search to combined semantic and business-data search scenarios - Determine how AI Vector Search supports GenAI pipelines and RAG |
| 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 |
| Working with JSON and Graph in Oracle AI Database | 20% | - Describe core graph concepts and graph analytic capabilities - Explain JSON and Oracle AI Database JSON capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case |
| Working with AI and Vector Foundations | 15% | - Describe AI, AGI, and machine learning foundations - Apply vector distance and indexing concepts to similarity search needs - Explain vectors, embeddings, and the Oracle VECTOR data type |
Oracleは成功の会社で、さまざまな認証と試験を提供します。我々の参考資料は実際の試験によって、弊社の1z0-1195-26資料をアップグレードしています。あなたの持っているすべての商品は一年の無料更新を得られています。あなたももっと多くの時間があって1z0-1195-26試験をよく準備します。
質問 # 37
What describes the role of the Database Actions menu in Autonomous AI Database?
正解：A
解説：
Database Actions serves as the primary web-based launchpad for built-in Autonomous AI Database tooling.
Oracle describes Database Actions, also known as SQL Developer Web, as an integrated interface containing development, data, administration, monitoring, and download features. From this environment, users can access SQL Worksheet, Data Modeler, REST, JSON tools, Oracle Machine Learning, Graph Studio, Oracle APEX, Data Studio functions, database-user administration, Data Pump, Performance Hub, and other database services.
Its role is therefore broader than any single database technology. Graph Studio is one tool accessible through the environment rather than a replacement for SQL. Network isolation is configured through Autonomous AI Database networking facilities such as private endpoints, not by treating Database Actions itself as a network- access mechanism. Likewise, vector-index settings represent only one narrow area of database functionality and do not define the Database Actions environment. Oracle also documents Data Studio as a feature area accessed through Database Actions, reinforcing the concept of Database Actions as a common launchpad for multiple specialized workspaces. The uploaded question set marks the central built-in-tools location as the correct response.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Database Actions Launchpad, development tools, Data Studio, administration, and monitoring.
質問 # 38
What is OSON in Oracle AI Database JSON support?
正解：C
解説：
OSON is Oracle's optimized binary representation for JSON data. Oracle AI Database uses OSON as the native storage representation of the SQL JSON data type. Unlike textual JSON stored in VARCHAR2, CLOB, or BLOB values, native JSON data does not need to be repeatedly parsed from character representation for common processing operations. Oracle states that OSON is optimized for fast query and update operations in both the Oracle AI Database server and supported database clients.
The practical advantage is that applications retain JSON's flexible document model while gaining database- native processing efficiency, SQL integration, indexing capabilities, and transactional control. OSON therefore concerns the physical/optimized representation of JSON data, not the operational scheduling of JSON collections or the visualization of graph structures. It is also unrelated to the SQL Worksheet, which is a Database Actions development interface for executing SQL and PL/SQL. The uploaded question explicitly identifies "An optimized binary format for JSON storage" as the correct answer.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - native JSON data type, OSON binary JSON representation, JSON query and update processing.
質問 # 39
Modern applications often need to work with relational data, JSON documents, graph relationships, and vector embeddings. What challenge does using a different specialized database for each need create?
正解：B
解説：
Using a different point-solution database for each data model can create data silos , increasing integration, synchronization, governance, and operational complexity. The uploaded assessment identifies option D as correct. Oracle AI Database 26ai is explicitly positioned as a converged database platform supporting AI, graph, document, spatial, relational, and other application models within one database architecture.
The problem with separate specialized stores is that an application may need to duplicate relational records into a document database, copy embeddings into a vector database, and maintain relationships in a graph database. These copies must remain synchronized as source data changes. Security controls, backups, monitoring, patching, access policies, and application integrations may also differ across platforms.
Oracle's converged model instead allows different representations and workloads to operate on centrally governed data. Oracle specifically notes that the converged platform provides synergy among multiple data models and enables different types of information to be joined and manipulated together.
Therefore, separate databases do not automatically share security or eliminate transformation. Those are precisely the architectural burdens that convergence is intended to reduce.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - converged database strategy and elimination of data silos.
質問 # 40
A recruiting application must find the top job postings that semantically match a candidate's resume, but only in the candidate's city. How should the application meet this requirement?
正解：C
解説：
The correct Oracle AI Vector Search design is a single SQL query combining the relational city restriction with vector-distance ranking . The uploaded source explicitly identifies option C. Oracle AI Database's native VECTOR type enables vector similarity searches without moving business information to a separate vector database. Oracle specifically states that vector searches can be combined with sophisticated business- data searches using SQL and the capabilities of its converged database.
The recruiting system can store an embedding of each job description alongside ordinary relational attributes such as city, employer, salary, employment type, and status. The candidate's resume or search request is converted into a query vector. SQL can then apply a predicate such as city = :candidate_city, calculate vector distance against qualifying job embeddings, order the result by that distance, and return the top matches.
Exporting postings to another vector platform unnecessarily introduces data movement and loses the direct integration with current relational attributes. Graph edge labels are intended for relationship modeling, not semantic document representation. "Early maintenance" is unrelated to vector ranking.
The key Oracle AI Database principle being tested is semantic vector retrieval and conventional relational business filtering within one SQL operation .
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - vector-distance ordering, relational predicates, top-K retrieval, and converged SQL.
質問 # 41
Which set of tools is specifically highlighted as available from Database Actions?
正解：D
解説：
The Database Actions environment specifically includes SQL, Data Modeler, REST, JSON, Oracle Machine Learning, and Oracle APEX , making option C correct. The uploaded assessment identifies the same tool set. Oracle's current Autonomous AI Database documentation describes Database Actions as a web- based interface for development, data tooling, administration, and monitoring. Its Development area includes SQL, Data Modeler, REST, JSON, Charts, Scheduling, Oracle Machine Learning, Spatial Studio, Graph Studio, and Oracle APEX.
SQL provides the browser-based worksheet for SQL and PL/SQL execution. Data Modeler supports database modeling and diagramming. REST provides tools for database REST services and APIs. JSON provides facilities for working with JSON collections and documents. Oracle Machine Learning exposes integrated ML development capabilities, while APEX launches Oracle's low-code application-development environment.
Oracle's Machine Learning documentation likewise identifies Database Actions as the entry point for Oracle Machine Learning.
The remaining choices largely describe OCI infrastructure or security-management services rather than Database Actions development tools. Compute, block storage, load balancing, IAM, billing, private endpoints, and Vault are managed elsewhere in OCI.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Database Actions Launchpad and Development tools.
質問 # 42
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当社OracleのウェブサイトIt-Passportsは非常に安全で定期的なプラットフォームです。 第一に、1z0-1195-26試験トレントの購入プロセス中に会社のウェブサイトのセキュリティを保証します。 第二に、1z0-1195-26模擬テストの購入に関するすべての顧客情報については、専門の担当者が管理し、情報開示は一切行われません。 最後になりましたが、最も重要なのは、1z0-1195-26試験の教材には、98％から100％の高い合格率に基づく高品質のメリットがあります。 Oracle AI Database Foundations Associateデータは他の言葉よりも雄弁です。 1z0-1195-26トレーニング準備に自信を持ってください。
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