Itcertkr에서 Oracle 1z0-1195-26 덤프를 다운받아 공부하시면 가장 적은 시간만 투자해도Oracle 1z0-1195-26시험패스하실수 있습니다. Itcertkr에서Oracle 1z0-1195-26시험덤프를 구입하시면 퍼펙트한 구매후 서비스를 제공해드립니다. Oracle 1z0-1195-26덤프가 업데이트되면 업데이트된 최신버전을 무료로 제공해드립니다. 시험에서 불합격성적표를 받으시면 덤프구매시 지불한 덤프비용은 환불해드립니다.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Working with AI and Vector Foundations | 15% | - Describe AI, AGI, and machine learning foundations - Explain vectors, embeddings, and the Oracle VECTOR data type - Apply vector distance and indexing concepts to similarity search needs |
| Topic 2: Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Apply AI Vector Search to combined semantic and business-data search scenarios - Describe Select AI in Autonomous AI Database - Determine how AI Vector Search supports GenAI pipelines and RAG |
| Topic 3: 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 |
| 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: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Create an Autonomous AI Database Serverless instance for a basic workload - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy |
| Topic 6: Working with JSON and Graph in Oracle AI Database | 20% | - Explain JSON and Oracle AI Database JSON capabilities - Describe core graph concepts and graph analytic capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case |
Itcertkr는 Oracle인증관련덤프를 제공하는 최고의 업체입니다, 덤프들은 Itcertkr의 베터랑의 전문가들이 오랜 풍부한 경험과 1z0-1195-26지식으로 만들어낸 최고의 제품입니다. 그리고 우리는 온라인무료 서비스도 제공되어 제일 빠른 시간에 소통 상담이 가능합니다.
질문 # 21
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
정답：D
설명：
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.
질문 # 22
A data analyst wants to discover data, load it into the platform, and prepare it for later use without leaving the built-in tools. Which workspace fits this task?
정답：C
설명：
Data Studio is the correct built-in workspace because its purpose directly covers data discovery, loading, preparation, transformation, analysis, cataloging, sharing, and related self-service workflows. The uploaded source designates Data Studio as the correct answer. Oracle's current Autonomous AI Database documentation states that Data Studio can be used to load, discover, catalog, transform, analyze, share, enrich, and automate data workflows through a web-based interface.
Data Studio is available through Database Actions and contains specialized tools including Data Load , Catalog , Data Transforms , Data Analysis , and Data Insights . Data Load can ingest or link data from local files, remote databases, cloud storage, and live feeds, while Catalog supports searching, discovering, inspecting, and managing available data assets.
OCI Vault addresses secrets and encryption-key management rather than data preparation.
VECTOR_DISTANCE is an individual SQL function used for vector comparisons, not an integrated analyst workspace. A Property Graph View defines a graph representation over data but likewise does not provide general discovery and loading functionality.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Data Studio, Data Load, Catalog, Data Transforms, and self-service data preparation.
질문 # 23
A manufacturer needs graph analysis that reflects inserts and updates from operational tables immediately.
How does a Property Graph View support this requirement?
정답：B
설명：
A Property Graph View provides a graph interpretation directly over data stored in relational database tables rather than requiring a separately maintained copy of that data. Oracle documents the Property Graph View as a view-like object containing metadata describing vertices, edges, labels, keys, and properties. Because the underlying graph information remains in the referenced relational tables, modifications to those tables are immediately reflected when the graph is queried.
This architecture directly satisfies the manufacturer's requirement. When operational applications insert or update rows in the underlying objects, graph queries subsequently operate against the updated relational data through the graph metadata definition. There is no required intermediate conversion to JSON and no scheduled batch refresh of a separate graph copy. Oracle also supports direct graph queries against database- resident property graph structures, including pattern matching through database graph-query facilities.
A separate in-memory graph server can be used for certain advanced analytics scenarios, but that does not change what a database Property Graph View fundamentally provides. The key exam distinction is metadata- based graph representation over existing database objects versus duplicated graph storage requiring synchronization. Therefore, option D precisely matches the required real-time operational behavior.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - Property Graph Views, metadata-based graph modeling, and relational-table integration.
질문 # 24
What is the main difference between Autonomous AI Database Serverless and Dedicated deployment choices?
정답：D
설명：
The fundamental distinction is that Serverless emphasizes simplicity and elasticity , whereas Dedicated provides isolated infrastructure and greater operational customization . This is the answer explicitly identified in the uploaded question set. Oracle documentation describes the Serverless model as ultra-simple and elastic: customers manage the Autonomous AI Database while Oracle manages the underlying Exadata infrastructure. Dedicated, by contrast, provides exclusive compute, storage, network, and database resources.
Oracle also characterizes Dedicated as a private-cloud-in-public-cloud deployment model with high levels of security isolation and governance. Dedicated environments can support customizable operational policies involving workload placement, update scheduling, availability, capacity usage, and other infrastructure-level concerns. Serverless removes much of that infrastructure planning and is therefore well suited to organizations prioritizing rapid provisioning and elastic consumption.
Neither deployment is restricted exclusively to JSON or relational workloads, and the distinction is not primarily about available developer SQL tools. Option A reverses the infrastructure characteristics: it is Dedicated-not Serverless-that supplies the isolated dedicated resource model.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Serverless versus Dedicated deployment architecture.
질문 # 25
A development team wants to receive patches before the regular maintenance schedule so they can validate changes early.
Which maintenance option should they select?
정답：A
설명：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Oracle Autonomous AI Database supports Regular and Early patch levels or maintenance schedules. The Early option applies patches before the Regular schedule so development and test systems can validate upcoming changes before production systems receive them. Oracle's current documentation states that Early patches are applied one week before the Regular scheduled patch and explicitly recommends Early for development and test databases when organizations want advance validation. Regular follows the normal maintenance cycle. "Late maintenance" and "Application-controlled maintenance" are not the applicable patch-level choices for this Serverless scenario. Because the team specifically wants patches before the regular schedule for early validation, the correct selection is Early maintenance. This falls under Autonomous AI Database operational basics, maintenance, and patch-management concepts. Oracle Docs
질문 # 26
......
Oracle 1z0-1195-26인증시험은 전문적인 관련지식을 테스트하는 인증시험입니다. Itcertkr는 여러분이Oracle 1z0-1195-26인증시험을 통과할 수 잇도록 도와주는 사이트입니다. 여러분은 응시 전 저희의 문제와 답만 잘 장악한다면 빠른 시일 내에 많은 성과 가 있을 것입니다.
1z0-1195-26시험대비 최신 공부자료: https://www.itcertkr.com/1z0-1195-26_exam.html