当社の製品で使用されているテストソフトウェアは、Windowsの1z0-1195-26学習教材に最適です。これにより、コンピューターで最高の学習スタイルを楽しむことができます。また、1z0-1195-26認定ガイドでは、最新の科学技術を使用して、権威ある研究材料ネットワーク学習の新しい要件を満たしています。従来の学習方法とは異なり、1z0-1195-26学習教材の大きな利点は、ユーザーが学習計画を柔軟に調整できることです。 1z0-1195-26テスト問題の新しいデザインが、ユーザーの学習をより面白く、カラフルにすることを願っています。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Autonomous AI Database and Tools | ~16% | - Built-in management and query tools - Core features of Autonomous AI Database - Shared vs dedicated infrastructure |
| Topic 2: 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 |
| Topic 3: Oracle Machine Learning and AI Integration | ~15% | - Oracle Data Studio and visualization - AI agents and LLM integration - In-database machine learning algorithms |
| Topic 4: MySQL HeatWave and NoSQL Services | ~11% | - MySQL HeatWave architecture and analytics - Oracle NoSQL Database features and use cases |
| Topic 5: Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Exadata architecture and features - Database Cloud Service (DBCS) characteristics |
| Topic 6: Converged Database — Multi-Model and AI Capabilities | ~15% | - Select AI and natural language querying - JSON, Graph, Spatial, and key-value data support - Oracle AI Vector Search concepts |
| Topic 7: 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 |
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質問 # 39
Which pair correctly matches an AI domain to an example?
正解：C
解説：
Vision - image classification is the correctly matched AI domain and use case. The uploaded source explicitly identifies option A as correct. Oracle Cloud Infrastructure Vision documentation confirms that Vision performs image analysis and includes image-classification capabilities for identifying objects and scene-based characteristics in images.
The distinction among the answer choices is based on the type of input being analyzed and the objective of the AI model. Computer vision works with images and visual content; classification assigns labels or categories based on visual characteristics. Language capabilities operate primarily on natural-language text and support functions such as entity recognition, sentiment analysis, text classification, and key-phrase extraction. Therefore, object detection in photographs belongs to vision rather than language.
Likewise, forecasting predicts future numerical or temporal outcomes from historical patterns; product- demand prediction is a typical forecasting scenario. Speech focuses on spoken audio, such as transcription or speech recognition, rather than business-demand prediction.
Option A is therefore the only domain/example relationship that is semantically and technically aligned.
Oracle Vision explicitly supports image classification, making the mapping unambiguous.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - AI domains, vision, language, speech, forecasting, and practical AI use cases.
質問 # 40
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
正解：D
質問 # 41
Which JSON feature helps represent repeating child data inside one document?
正解：B
解説：
JSON arrays and nested objects provide the hierarchical structure required to represent repeating or composite child information within a single JSON document. The uploaded question set explicitly identifies this answer. Oracle AI Database supports standard JSON value types including objects and arrays. An object contains named property/value members, while an array contains an ordered sequence of JSON values.
Because array elements can themselves be objects or additional arrays, applications can represent complex parent-child structures naturally within one document.
For example, a customer document can contain an addresses array with multiple address objects, or an order can contain an items array where every element contains product, quantity, and price properties. This avoids artificially flattening inherently hierarchical information.
A scalar property is appropriate for a single value and therefore cannot naturally represent repeated child records. Merely storing an external identifier does not embed the child information in the document. Creating a separate standalone JSON document for every child would also fail the requirement to represent the repeating data inside one document .
Study Guide reference: Working with JSON and Graph in Oracle AI Database - JSON objects, arrays, hierarchical documents, and nested JSON structures.
質問 # 42
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 # 43
A retailer wants to find products semantically similar to a shopper's description, but only from items that are in stock and sold in the shopper's region. Which design meets this requirement?
正解：B
解説：
Oracle AI Vector Search is designed to combine semantic similarity with conventional business predicates inside the same SQL statement. Oracle's native VECTOR data type and vector-distance operators allow embeddings to coexist with relational attributes such as inventory status, region, category, price, or security classification. The application can therefore restrict rows using ordinary SQL predicates-for example, in_stock = 'Y' and region = :region-while ranking qualifying products using vector similarity or distance.
Oracle explicitly positions the converged database architecture as enabling vector similarity searches together with relational, JSON, graph, text, and spatial criteria in a single database query.
JSON Duality Views are not a prerequisite for semantic search, and a property graph does not replace the vector engine. Performing vector retrieval first and filtering unavailable inventory in application code is also inferior because it wastes retrieval capacity and can distort the top-K result set. Applying business filters and semantic ranking together keeps processing close to the data and produces the appropriate eligible top matches. The uploaded source identifies this integrated SQL design as the correct choice.
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - vector similarity search combined with relational filtering.
質問 # 44
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