現在、試験銀行がシミュレーションテストを提供するような統合システムを持っていることはほとんどありません。 1z0-1195-26学習ツールについて学習した後、実際の1z0-1195-26試験を刺激することの重要性が徐々に認識されます。この機能により、練習システムがどのように動作するかを簡単に把握し、1z0-1195-26試験に関する中核的な知識を得ることができます。さらに、実際の試験環境にいるときは、質問への回答の速度と品質を制御し、エクササイズの良い習慣を身に付けることを学ぶことができます。そのため、1z0-1195-26試験に合格できます。
| 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 |
| 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 |
| 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% | - 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 |
| 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 |
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Create an Autonomous AI Database Serverless instance for a basic workload |
1z0-1195-26の有用なテストガイド資料は、最も重要な情報を最も簡単な方法でクライアントに提示するため、1z0-1195-26の有用なテストガイドを学習するための時間とエネルギーはほとんど必要ありません。クライアントは、テストの学習と準備に20〜30時間しかかかりません。仕事や学習などで忙しい人にとっては、これは良いニュースです。なぜなら、テストの準備に十分な時間がないことを心配する必要がなく、主なことをゆっくりとできるからです。 1z0-1195-26学習実践ガイドをご覧ください。ですから、1z0-1195-26試験の教材の大きな利点であり、クライアントにとって非常に便利です。
質問 # 16
A manufacturer needs graph analysis that reflects inserts and updates from operational tables immediately.
How does a Property Graph View support this requirement?
正解：A
解説：
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.
質問 # 17
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?
正解：A
解説：
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
質問 # 18
What is the operational benefit of using a converged database instead of several point-solution databases?
正解：B
解説：
A converged database can reduce operational complexity because multiple data models and workload types can use a common database platform with a more consistent approach to security, upgrades, patching, and maintenance . The uploaded question source explicitly identifies option C. Oracle documentation states that Oracle AI Database is a converged, multimodel database and specifically highlights a common approach for security, upgrades, patching, and maintenance.
With separate point-solution databases, organizations may need distinct administrator skill sets, identity configurations, encryption mechanisms, backup procedures, monitoring systems, patch schedules, and replication pipelines. Consolidating appropriate workloads onto a converged database can remove portions of that duplicated operational footprint while allowing relational, JSON, graph, spatial, vector, and other capabilities to work together.
However, convergence does not mean security policies can stop being reviewed. Governance remains necessary. It also does not require every workload to use an identical physical schema or application-access mechanism; Oracle supports multiple data models and APIs precisely because applications have different access requirements.
Option D describes a disadvantage of fragmented point solutions: separate databases frequently introduce synchronization processes. The converged architecture is intended to reduce, rather than require, such duplication.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - converged architecture, operational simplification, security, patching, and maintenance.
質問 # 19
How can developers access JSON Duality Views?
正解：B
解説：
Developers can access JSON-Relational Duality Views using document-oriented interfaces, including Oracle AI Database API for MongoDB , as well as SQL-based database interfaces. The uploaded source explicitly identifies "MongoDB-compatible APIs or SQL" as correct. Oracle documentation confirms that duality views expose relational table data as JSON documents and that applications can interact with the same underlying data either document-centrically or relationally.
With the MongoDB-compatible API, the duality view can be treated as a document collection by applications using familiar MongoDB drivers and development patterns. At the same time, because the authoritative data remains in Oracle relational tables, SQL and other relational capabilities can operate directly on the same information. This is a primary architectural advantage of JSON-Relational Duality.
No export into a separate document store is necessary; doing so would reintroduce data duplication and synchronization problems that duality views are designed to avoid. Graph visualization is unrelated to document access, and although APEX applications can consume database data, APEX-specific PL/SQL packages are not the principal interface defining duality-view access.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - JSON Duality Views, Oracle AI Database API for MongoDB, SQL access, and unified relational/document development.
質問 # 20
You just provisioned a Serverless database and need to confirm when application teams can begin connecting to it. When is the database ready for connections?
正解：B
解説：
The database should be treated as ready after provisioning completes and its lifecycle state becomes Available
. The uploaded question source explicitly identifies this state as the correct readiness point. Oracle Autonomous AI Database documentation uses Available as the operational lifecycle state for an instance and requires that state for numerous database-management operations. Oracle's connection documentation then describes applications and client tools connecting to an existing Autonomous AI Database through supported Oracle Net Services and built-in tools.
Selecting Create only initiates the provisioning workflow. OCI still has to allocate and configure the underlying service resources before the database becomes operational. Loading a JSON collection is an application-data activity performed after database availability and is unrelated to whether the database service itself is ready. Likewise, Data Studio is a built-in data-management environment; creation or use of a Data Studio workflow is not a prerequisite for normal database connectivity.
For certification purposes, the decisive distinction is between provisioning initiated and provisioning completed . The Available lifecycle status indicates that the service has completed the provisioning process sufficiently for normal database use.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Serverless provisioning, lifecycle states, and database connectivity.
質問 # 21
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