当社の製品には多くの面で多くのメリットがあり、1z0-1195-26練習エンジンの品質を保証できます。まず、経験豊富な専門家チームが実際の試験に基づいて入念に編集します。第二に、1z0-1195-26学習教材の言語と内容の両方がシンプルです。このコンテンツは焦点を強調し、洗練された1z0-1195-26の質問と回答を使用するキーをつかみ、学習者が最小限の実践で最も重要な情報を習得できるようにします。 3つ目は、学習者が教材を学習し、試験の準備をするのに役立つさまざまな機能を提供することです。
| Section | Weight | Objectives |
|---|---|---|
| Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Exadata architecture and features - Database Cloud Service (DBCS) characteristics |
| Oracle Machine Learning and AI Integration | ~15% | - In-database machine learning algorithms - Oracle Data Studio and visualization - AI agents and LLM integration |
| Security, Resilience, and Cloud Integration | ~21% | - High availability, backup, and disaster recovery - Cloud-native database services and deployment strategies - Database security architectures and data protection |
| Converged Database — Multi-Model and AI Capabilities | ~15% | - JSON, Graph, Spatial, and key-value data support - Select AI and natural language querying - Oracle AI Vector Search concepts |
| MySQL HeatWave and NoSQL Services | ~11% | - Oracle NoSQL Database features and use cases - MySQL HeatWave architecture and analytics |
| Data Management and Oracle Data Platform Overview | ~11% | - Data management concepts and data types - Modern data platform value and architecture - Oracle Data Strategy and multi-cloud deployment models |
| Autonomous AI Database and Tools | ~16% | - Shared vs dedicated infrastructure - Built-in management and query tools - Core features of Autonomous AI Database |
Oracle証明書を取得することは、あなたのキャリアにおける地位を高める素晴らしく迅速な方法です。 1z0-1195-26試験に合格するというこの目標を達成するには、外部の支援が必要です。弊社が市場で最も人気のあるベンダーであるため、このリンクをクリックすると幸運です。私たちはこのキャリアに10年以上携わっており、1z0-1195-26試験問題では、夢のOracle認定を得るための支援を受けるだけでなく、オンラインで一流のサービスを楽しむことができます。
質問 # 41
A recruiting application must find the top job postings that semantically match a candidate's resume, but only in the candidate's city. How should the application meet this requirement?
正解：A
解説：
The correct Oracle AI Vector Search design is a single SQL query combining the relational city restriction with vector-distance ranking . The uploaded source explicitly identifies option C. Oracle AI Database's native VECTOR type enables vector similarity searches without moving business information to a separate vector database. Oracle specifically states that vector searches can be combined with sophisticated business- data searches using SQL and the capabilities of its converged database.
The recruiting system can store an embedding of each job description alongside ordinary relational attributes such as city, employer, salary, employment type, and status. The candidate's resume or search request is converted into a query vector. SQL can then apply a predicate such as city = :candidate_city, calculate vector distance against qualifying job embeddings, order the result by that distance, and return the top matches.
Exporting postings to another vector platform unnecessarily introduces data movement and loses the direct integration with current relational attributes. Graph edge labels are intended for relationship modeling, not semantic document representation. "Early maintenance" is unrelated to vector ranking.
The key Oracle AI Database principle being tested is semantic vector retrieval and conventional relational business filtering within one SQL operation .
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - vector-distance ordering, relational predicates, top-K retrieval, and converged SQL.
質問 # 42
What is an embedding in the context of AI Vector Search?
正解：B
解説：
An embedding is a numerical vector representation of the meaning or characteristics of some source content.
Oracle describes vector embeddings as mathematical vector representations that capture semantic meaning for content such as words, documents, images, audio, or other data objects. The embedding model transforms the source input into an ordered sequence of numerical values, placing semantically related items close together within a multidimensional vector space.
This numerical representation enables AI Vector Search. Instead of comparing documents solely by literal keyword equality, Oracle can calculate mathematical distances between embeddings. Smaller distances generally identify objects that are semantically more closely related according to the embedding model. This supports capabilities such as semantic document retrieval, recommendation systems, image similarity, RAG, and natural-language search.
An embedding is therefore not a backup mechanism, database maintenance configuration, or graph label.
Those concepts belong to unrelated database subsystems. The crucial certification distinction is that the original content and its embedding are different representations: the content may be text, image, audio, or another object, while its embedding is a numeric vector representing learned semantic characteristics. The uploaded assessment identifies "a sequence of numbers that represents semantic content" as the correct description.
Study Guide reference: Working with AI and Vector Foundations - embeddings, semantic representations, vector spaces, and similarity search.
質問 # 43
Which offering is positioned for JSON-centric workloads and includes Oracle Database API for MongoDB?
正解：D
解説：
Autonomous AI JSON Database is the Oracle Autonomous AI Database offering specifically optimized for document-centric and JSON-centric application workloads. Oracle documentation identifies Autonomous AI JSON Database as optimized for JSON document workloads while retaining the underlying converged capabilities of Oracle AI Database. It supports NoSQL-style access patterns while still permitting applications and administrators to use SQL against the same underlying information.
A key capability is Oracle AI Database API for MongoDB. This interface enables applications to connect to Autonomous AI Database using familiar MongoDB drivers and tools. Oracle translates MongoDB-compatible operations so developers with MongoDB development experience can work with collections of JSON documents residing in Oracle AI Database. The same database can also expose those documents through SQL, PL/SQL, SODA, and other Oracle interfaces.
Autonomous AI Transaction Processing supports mixed and transactional application workloads but is not the specifically positioned JSON-centric offering in this question. Autonomous AI Lakehouse targets analytical and lakehouse-oriented workloads, while Oracle APEX Service is a low-code application platform rather than the JSON database workload type itself. Therefore, Autonomous AI JSON Database is the precise match. The uploaded question set also marks option C as correct.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - Autonomous AI JSON Database and Oracle AI Database API for MongoDB.
質問 # 44
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?
正解：B
解説：
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.
質問 # 45
Which pair correctly matches an AI domain to an example?
正解：A
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 # 46
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この競争が激しい社会では、GoShikenはたくさんの受験生の大好評を博するのは我々はいつも受験生の立場で試験ソフトを開発するからです。例えば、我々のよく発売されているOracleの1z0-1195-26試験ソフトは大量の試験問題への研究によって作れることです。試験に失敗したら全額で返金するという承諾があるとは言え、弊社の商品を利用したほとんどの受験生は試験に合格しました。
1z0-1195-26過去問無料: https://www.goshiken.com/Oracle/1z0-1195-26-mondaishu.html