지금 같은 경쟁력이 심각한 상황에서Oracle 1z0-1195-26시험자격증만 소지한다면 연봉상승 등 일상생활에서 많은 도움이 될 것입니다.Oracle 1z0-1195-26시험자격증 소지자들의 연봉은 당연히Oracle 1z0-1195-26시험자격증이 없는 분들보다 높습니다. 하지만 문제는Oracle 1z0-1195-26시험패스하기가 너무 힘듭니다. Pass4Test는 여러분의 연봉상승을 도와 드리겠습니다.
| Section | Weight | Objectives |
|---|---|---|
| Converged Database — Multi-Model and AI Capabilities | ~15% | - JSON, Graph, Spatial, and key-value data support - Oracle AI Vector Search concepts - Select AI and natural language querying |
| MySQL HeatWave and NoSQL Services | ~11% | - MySQL HeatWave architecture and analytics - Oracle NoSQL Database features and use cases |
| Autonomous AI Database and Tools | ~16% | - Built-in management and query tools - Shared vs dedicated infrastructure - Core features of Autonomous AI Database |
| Data Management and Oracle Data Platform Overview | ~11% | - Oracle Data Strategy and multi-cloud deployment models - Data management concepts and data types - Modern data platform value and architecture |
| Oracle Machine Learning and AI Integration | ~15% | - AI agents and LLM integration - Oracle Data Studio and visualization - In-database machine learning algorithms |
| Security, Resilience, and Cloud Integration | ~21% | - Database security architectures and data protection - High availability, backup, and disaster recovery - Cloud-native database services and deployment strategies |
| Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Exadata architecture and features - Database Cloud Service (DBCS) characteristics |
1z0-1195-26인증시험은 IT업계에 종사하고 계신 분이시라면 최근 많은 인기를 누리고 있다는 것을 알고 계실것입니다. 1z0-1195-26인증시험을 패스하여 자격증을 취득하는데 가장 쉬운 방법은 Pass4Test에서 제공해드리는 1z0-1195-26덤프를 공부하는 것입니다. Oracle 1z0-1195-26덤프에 있는 문제와 답만 기억하시면 1z0-1195-26시험을 패스하는데 많은 도움이 됩니다.덤프구매후 최신버전으로 업데이트되면 업데이트버전을 시스템 자동으로 구매시 사용한 메일주소로 발송해드려 덤프유효기간을 최대한 길게 연장해드립니다.
질문 # 37
What does Select AI enable in Autonomous AI Database?
정답：C
설명：
Select AI enables users to interact with Autonomous AI Database by expressing requests in natural language rather than manually constructing SQL. Oracle documents that Select AI uses generative AI and large language models to convert natural-language input into Oracle SQL. Depending on the requested action, the generated SQL can be displayed, explained, executed, or its results can be transformed into a natural-language response. This makes database information accessible to users who understand the business question but may not know SQL syntax or the underlying schema.
Internally, Select AI can augment a prompt with schema metadata, interact with the configured LLM, generate SQL, run the query, and optionally narrate the resulting data. It also extends beyond NL2SQL into Retrieval-Augmented Generation, conversations, and other generative-AI capabilities. It does not replace relational SQL with graph pattern matching, manage encryption-key rotation, or provision Autonomous AI Database infrastructure. Those are unrelated database administration or graph functions. The supplied question bank explicitly marks the natural-language data-query capability as correct.
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - Select AI, natural-language interaction, NL2SQL, and LLM integration.
질문 # 38
Which sequence matches a simple RAG pipeline?
정답：D
설명：
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.
질문 # 39
Which task is a common use of graph analytics?
정답：D
설명：
Determining communities or connected clusters in a network is a canonical graph-analytics use case. The uploaded source identifies option D as the correct response. Oracle Property Graph documentation explicitly lists finding communities , influencers, recommendations, graph traversal, pattern matching, and path finding among typical graph-analysis operations. Oracle AI Database 26ai documentation also identifies Community Detection as a supported graph-analysis algorithm.
Community detection examines topology to identify groups of vertices that are more strongly connected with one another than with the remainder of the network. Examples include identifying customer communities in social networks, coordinated groups in fraud investigations, clusters of interconnected devices in telecommunications, or related entities in knowledge graphs.
The other choices deliberately discard the characteristic that makes graph analytics useful: relationships.
Listing unrelated records and sorting a scalar column are standard tabular operations. Storing relationships as isolated text values prevents the database from traversing and analyzing those relationships as graph edges.
Graph analytics becomes valuable when the question concerns connectivity, paths, neighborhoods, influence, centrality, clusters, or structural patterns rather than merely individual records and attributes.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - graph analytics, community detection, connectivity, and network analysis.
질문 # 40
How does Oracle APEX help developers build applications with Oracle Database data?
정답：C
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 # 41
What does a JSON Duality View enable?
정답：B
설명：
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.
질문 # 42
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불과 1,2년전만 해도 Oracle 1z0-1195-26덤프를 결제하시면 수동으로 메일로 보내드리기에 공휴일에 결제하시면 덤프를 보내드릴수 없어 고객님께 페를 끼쳐드렸습니다. 하지만 지금은 시스템이 업그레이드되어Oracle 1z0-1195-26덤프를 결제하시면 바로 사이트에서 다운받을수 있습니다. Pass4Test는 가면갈수록 고객님께 편리를 드릴수 있도록 나날이 완벽해질것입니다.
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