今の多士済々な社会の中で、IT専門人士はとても人気がありますが、競争も大きいです。だからいろいろな方は試験を借って、自分の社会の地位を固めたいです。1z0-1195-26認定試験はOracleの中に重要な認証試験の一つですが、GoShikenにIT業界のエリートのグループがあって、彼達は自分の経験と専門知識を使ってOracle 1z0-1195-26認証試験に参加する方に対して問題集を研究続けています。
| Section | Weight | Objectives |
|---|---|---|
| Security, Resilience, and Cloud Integration | ~21% | - High availability, backup, and disaster recovery - Database security architectures and data protection - Cloud-native database services and deployment strategies |
| Converged Database — Multi-Model and AI Capabilities | ~15% | - Oracle AI Vector Search concepts - Select AI and natural language querying - JSON, Graph, Spatial, and key-value data support |
| Oracle Machine Learning and AI Integration | ~15% | - Oracle Data Studio and visualization - AI agents and LLM integration - In-database machine learning algorithms |
| MySQL HeatWave and NoSQL Services | ~11% | - MySQL HeatWave architecture and analytics - Oracle NoSQL Database features and use cases |
| Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Database Cloud Service (DBCS) characteristics - Exadata architecture and features |
| Data Management and Oracle Data Platform Overview | ~11% | - Oracle Data Strategy and multi-cloud deployment models - Modern data platform value and architecture - Data management concepts and data types |
| Autonomous AI Database and Tools | ~16% | - Built-in management and query tools - Core features of Autonomous AI Database - Shared vs dedicated infrastructure |
あなたはOracleの1z0-1195-26の資料を探すのに悩んでいますか。心配しないでください。私たちを見つけるのはあなたのOracleの1z0-1195-26試験に合格する保障からです。数年以来IT認証試験のためのソフトを開発している我々GoShikenチームは国際的に大好評を博しています。我々はOracleの1z0-1195-26のような重要な試験を準備しているあなたに一番全面的で有効なヘルプを提供します。
質問 # 31
A development team wants to receive patches before the regular maintenance schedule so they can validate changes early.
Which maintenance option should they select?
正解：D
解説：
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
質問 # 32
In a property graph, what are vertices and edges?
正解：D
質問 # 33
How does Oracle APEX help developers build applications with Oracle Database data?
正解：B
解説：
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.
質問 # 34
Which set of characteristics is commonly used to describe Autonomous AI Database?
正解：B
解説：
The uploaded question set identifies self-driving, self-securing, and self-repairing as the defining Autonomous AI Database characteristics. Oracle documentation uses the same terminology when describing Autonomous Database services.
Self-driving refers to automated database-management activities that traditionally require significant DBA intervention, including provisioning, tuning, optimization, backups, patching, and scaling. Self-securing encompasses automated security practices designed to protect database infrastructure and data, including encryption and security maintenance. Self-repairing describes automated availability and fault-management capabilities intended to reduce downtime and recover from infrastructure or database failures with minimal manual involvement.
These attributes are central to Oracle's Autonomous Database strategy because the service shifts routine infrastructure and database operations from customer-managed procedures toward automated cloud-service capabilities. This allows development and data teams to focus on application logic and business workloads rather than routine database administration.
The alternative combinations are not Oracle's established characterization. Autonomous AI Database does not promise autonomous application coding, licensing, documentation creation, or architectural design. Those terms incorrectly broaden the scope of database automation beyond the capabilities Oracle associates with the Autonomous platform.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Autonomous characteristics and automated database operations.
質問 # 35
What does the Oracle VECTOR data type enable?
正解：D
解説：
The Oracle VECTOR data type provides native database storage for vector values used by AI and machine- learning workloads. Oracle AI Database 26ai represents vectors as ordered numerical values with defined dimensionality and element formats. This allows vector embeddings representing text, images, audio, documents, or other content to reside directly alongside conventional business data rather than requiring a separate specialized vector database.
Native vector storage is foundational to Oracle AI Vector Search. Once embeddings are stored in VECTOR columns, SQL can apply vector-distance functions, perform exact or approximate similarity searches, create vector indexes, and combine semantic rankings with relational, JSON, text, spatial, or graph predicates.
Oracle emphasizes that keeping vectors with business data reduces data movement, lowers architecture complexity, and permits similarity searches against current transactional information.
The VECTOR type does not provide APEX page design-that is an Oracle APEX function. It does not universally validate JSON schemas, nor does it automatically convert relational tables into graph structures.
Those are separate Oracle Database capabilities. Consequently, native storage of vector values precisely describes its core function, consistent with the uploaded question source.
Study Guide reference: Working with AI and Vector Foundations - VECTOR data type, vector embeddings, vector columns, and AI Vector Search.
質問 # 36
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