IT인증자격증은 국제적으로 승인받는 자격증이기에 많이 취득해두시면 취업이나 승진이나 이직이나 모두 편해집니다. 다른 사람이 없는 자격증을 내가 가지고 있다는것은 실력을 증명해주는 수단입니다. Oracle인증 1z0-1195-26시험은 널리 승인받는 자격증의 시험과목입니다. Oracle인증 1z0-1195-26덤프로Oracle인증 1z0-1195-26시험공부를 하시면 시험패스 난이도가 낮아지고 자격증 취득율이 높이 올라갑니다.자격증을 많이 취득하여 취업이나 승진의 문을 두드려 보시면 빈틈없이 닫힌 문도 활짝 열릴것입니다.
| Section | Weight | Objectives |
|---|---|---|
| Oracle Machine Learning and AI Integration | ~15% | - Oracle Data Studio and visualization - In-database machine learning algorithms - AI agents and LLM integration |
| MySQL HeatWave and NoSQL Services | ~11% | - Oracle NoSQL Database features and use cases - MySQL HeatWave architecture and analytics |
| Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Exadata architecture and features - Database Cloud Service (DBCS) characteristics |
| Security, Resilience, and Cloud Integration | ~21% | - Cloud-native database services and deployment strategies - High availability, backup, and disaster recovery - Database security architectures and data protection |
| Data Management and Oracle Data Platform Overview | ~11% | - Data management concepts and data types - Oracle Data Strategy and multi-cloud deployment models - Modern data platform value and architecture |
| Autonomous AI Database and Tools | ~16% | - Shared vs dedicated infrastructure - Built-in management and query tools - Core features of Autonomous AI Database |
| Converged Database — Multi-Model and AI Capabilities | ~15% | - JSON, Graph, Spatial, and key-value data support - Select AI and natural language querying - Oracle AI Vector Search concepts |
>> 1z0-1195-26시험대비 최신버전 공부자료 <<
Oracle 1z0-1195-26인증덤프는 실제 1z0-1195-26시험의 가장 최근 시험의 기출문제를 기준으로 하여 만들어진 최고품질을 자랑하는 최고적중율의 시험대비자료입니다. 저희 1z0-1195-26덤프로 1z0-1195-26시험에 도전해보지 않으실래요? 1z0-1195-26시험에서 불합격 받을시 덤프비용은 환불해드리기에 부담없이 구매하셔도 됩니다.환불의 유일한 기준은 불합격 성적표이고 환불유효기간은 구매일로부터 60일까지입니다.
질문 # 32
How does Oracle APEX help developers build applications with Oracle Database data?
정답：D
설명：
Oracle APEX is Oracle's low-code application development platform and is tightly integrated with Oracle Database. Oracle documentation describes APEX as providing browser-based enterprise application development with direct access to Oracle Database data. Developers configure pages, components, workflows, data sources, reports, forms, and business logic declaratively through App Builder rather than constructing every layer manually.
APEX explicitly embraces SQL. Developers can use SQL and PL/SQL to query and manipulate database- resident information while leveraging declarative components to minimize conventional coding effort.
Because the application engine operates close to the database, applications also inherit Oracle capabilities for transactions, security, concurrency, availability, analytics, and data management. Autonomous AI Database includes Oracle APEX capabilities and allows developers to create APEX workspaces and applications directly against database data.
APEX is therefore neither a disconnected application platform nor a command-line scripting framework. It is also distinct from Graph Studio and graph analytics capabilities used for operations such as community detection. The defining characteristics relevant to this question are its browser-based low-code development environment and native access to Oracle Database through SQL. The uploaded question bank confirms option B.
Study Guide reference: Building Low-Code Applications and Agentic AI - Oracle APEX, App Builder, browser-based development, and SQL integration.
질문 # 33
What is OSON in Oracle AI Database JSON support?
정답：D
설명：
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.
질문 # 34
A business wants one data platform where semantic similarity, relational consistency, and SQL-based filtering all work together.
Which statement aligns with this design goal?
정답：B
설명：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Oracle AI Database's converged architecture is designed to keep vectors and conventional business data in the same database so semantic ranking can be combined directly with SQL predicates. Oracle's VECTOR data type enables vector similarity search inside the database, and Oracle explicitly documents combining business- data searches with AI vector similarity search using SQL and the broader converged engine. This preserves transactional consistency and avoids exporting data to a separate vector platform merely to perform semantic retrieval. Options A and C incorrectly separate relational filtering from vector retrieval, while option B incorrectly requires graph modeling. The intended architecture is therefore one database that combines relational filtering, consistency, and vector similarity. This is a core design principle under "Implementing Select AI and AI Vector Search in Autonomous AI Database." Oracle Docs
질문 # 35
A development team needs an Autonomous AI Database deployment that starts small, minimizes setup effort, and can scale easily. Which deployment choice fits this requirement?
정답：A
설명：
Serverless best satisfies requirements for minimal infrastructure setup, a small starting footprint, and elastic scaling. The uploaded source identifies option D as correct. Oracle documentation describes the Serverless deployment model as ultra-simple and elastic , with Oracle managing the Exadata infrastructure underneath the Autonomous AI Database service.
This means application teams can focus principally on database-level resources and workloads rather than first designing and administering dedicated infrastructure capacity. Autonomous AI Database Serverless is therefore well aligned with teams seeking rapid provisioning and the ability to adjust resources as workload requirements change.
Dedicated deployment addresses a different requirement profile. Oracle describes Dedicated as providing exclusive compute, storage, network, and database resources, with stronger infrastructure isolation, operational control, governance, and customization. Those characteristics are valuable for organizations requiring dedicated Exadata resources or greater infrastructure control, but they do not minimize initial capacity planning and infrastructure considerations in the way Serverless does.
A fixed-capacity alternative also contradicts the requirement for easy scaling. Therefore, the exam distinction is straightforward: Serverless emphasizes simplicity and elasticity; Dedicated emphasizes isolation and greater infrastructure control.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Serverless versus Dedicated deployment models.
질문 # 36
A business team wants to launch a no-code AI agent quickly. They prefer to start from a ready-made option and later refine the publishing workflow. Which Private Agent Factory capability path fits this need?
정답：B
설명：
Oracle AI Database Private Agent Factory is explicitly designed as a no-code environment for rapidly building, testing, and deploying intelligent agents. Oracle documents that Agent Factory supports pre-built agents, custom-built agents, and end-to-end workflows and includes curated agentic templates intended to accelerate implementation. Starting from one of these ready-made assets minimizes initial design work and is therefore the strongest match for a business team that prioritizes rapid deployment.
If additional customization becomes necessary, Agent Builder provides a visual no-code environment for constructing and refining agents and workflows from modular components. Oracle describes capabilities including drag-and-drop workflow construction, data connectors, LLM integration, APIs, custom agent creation, multi-agent orchestration, and reusable templates. This establishes a logical progression: begin with a pre-built agent/template to obtain functionality quickly, then move into Agent Builder when deeper customization or workflow tailoring is required. Starting from a completely blank agent would unnecessarily increase implementation effort, while a prompt-only prototype bypasses Agent Factory's governed agent capabilities. The source question likewise identifies the pre-built-to-Agent-Builder path as correct.
Study Guide reference: Building Low-Code Applications and Agentic AI - Private Agent Factory, pre-built agents, templates, and Agent Builder.
질문 # 37
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