1z0-1195-26認定試験の準備をするために、CertShiken の専門家たちは彼らの豊富な知識と実践を生かして特別なトレーニング資料を研究しました。CertShiken のOracleの1z0-1195-26問題集はあなたが楽に試験に受かることを助けます。CertShiken のOracleの1z0-1195-26練習テストは1z0-1195-26試験問題と解答、 1z0-1195-26 問題集、1z0-1195-26 書籍や1z0-1195-26勉強ガイドに含まれています。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Database Cloud Service (DBCS) characteristics - Exadata architecture and features |
| Topic 2: Data Management and Oracle Data Platform Overview | ~11% | - Modern data platform value and architecture - Data management concepts and data types - Oracle Data Strategy and multi-cloud deployment models |
| Topic 3: MySQL HeatWave and NoSQL Services | ~11% | - Oracle NoSQL Database features and use cases - MySQL HeatWave architecture and analytics |
| Topic 4: Autonomous AI Database and Tools | ~16% | - Shared vs dedicated infrastructure - Core features of Autonomous AI Database - Built-in management and query tools |
| Topic 5: Converged Database — Multi-Model and AI Capabilities | ~15% | - Select AI and natural language querying - Oracle AI Vector Search concepts - JSON, Graph, Spatial, and key-value data support |
| Topic 6: 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 7: Oracle Machine Learning and AI Integration | ~15% | - AI agents and LLM integration - In-database machine learning algorithms - Oracle Data Studio and visualization |
1z0-1195-26試験問題は、学習結果を検出するためのさまざまな自己学習および自己評価機能を備えたソフトウェアを提供します。統計レポート機能は、学生が弱点を見つけて対処するのに役立つように提供されています。当社のソフトウェアには、時間制限やシミュレートされたテスト機能など、多くの新しい機能も搭載されています。速度を調整してアラートを維持できる1z0-1195-26テストガイドでシミュレーションテストタイマーを設定したら、知識を習得するために心を傾けることができます。この関数が1z0-1195-26試験の合格に役立つことは間違いありません。
質問 # 21
Which pair correctly matches an AI domain to an example?
正解：D
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 # 22
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
正解：B
解説：
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.
質問 # 23
How is Oracle APEX described?
正解：A
解説：
Oracle APEX is Oracle's low-code application development platform , making option C correct. The uploaded source explicitly identifies this characterization. Oracle's current documentation defines APEX as a complete low-code platform for rapidly building secure, scalable enterprise applications. Developers work primarily through the browser-based App Builder and configure declarative components, pages, features, and data sources rather than manually writing the entire application stack.
APEX is built as part of Oracle Database and provides direct access to Oracle Database data. Its model-driven architecture allows developers to use declarative metadata for common application functionality while retaining SQL, PL/SQL, JavaScript, REST, and other extension mechanisms when custom logic is necessary.
The incorrect options describe entirely different Oracle technologies. Graph analytics and property graphs are Oracle Database graph capabilities, not the definition of APEX. Encryption-key lifecycle management belongs to database security and services such as OCI Vault. Oracle's optimized binary representation for JSON storage is OSON.
The exam-level distinction is therefore direct: APEX addresses rapid application creation, especially data- centric enterprise applications, through a browser-based low-code development model tightly integrated with Oracle Database.
Study Guide reference: Building Low-Code Applications and Agentic AI - Oracle APEX, low-code development, App Builder, and database-driven applications.
質問 # 24
How can developers create vectors for data objects in Oracle AI Database 26ai?
正解：D
解説：
Oracle AI Database 26ai provides native SQL and PL/SQL facilities for generating vector embeddings from source data. A principal example is the VECTOR_EMBEDDING SQL function, which generates an embedding by applying an embedding or feature-extraction model to an input value. Oracle also provides vector utilities such as UTL_TO_EMBEDDING, DBMS_VECTOR, and DBMS_VECTOR_CHAIN for vectorization, chunking, embedding generation, similarity-search pipelines, and integration with supported embedding providers.
When an embedding model is imported into Oracle AI Database-for example, in supported ONNX form- the database can perform text-to-vector transformation internally. Oracle also supports accessing external embedding providers through REST where appropriate, but sending all data to a separate vector database or service is not a prerequisite. Keeping vectorization and vector storage within Oracle AI Database can reduce data movement and enables embeddings to remain integrated with the underlying business objects.
Vectors also do not need to be manually represented as JSON documents, and Property Graph Views serve a different purpose: modeling entities and relationships. Consequently, the built-in vectorization capability is the direct Oracle-native mechanism described by the question. The uploaded source explicitly identifies option A as correct.
Study Guide reference: Working with AI and Vector Foundations - vector generation, VECTOR_EMBEDDING, vector utilities, embedding models, and native AI Vector Search.
質問 # 25
What happens after a user asks a business question with Select AI?
正解：C
解説：
Select AI automates the interaction among the user's natural-language prompt, database metadata, the configured large language model, generated SQL, and returned results. The uploaded assessment therefore correctly identifies option D. Oracle's Select AI documentation states that Autonomous AI Database processes the natural-language prompt, augments it with relevant metadata, interacts with an LLM, generates SQL, and can execute that SQL to return information.
Schema metadata is particularly important. Oracle can augment the prompt with table names, column names and data types, comments, annotations, constraints, and relationship information. This provides the LLM with database context and improves SQL generation while reducing hallucination risk.
Depending on the Select AI action, the service can display generated SQL, execute it, explain it, narrate query results in natural language, perform RAG against vector stores, or communicate directly with an LLM.
Select AI does not disable SQL; SQL remains fundamental to natural-language-to-SQL processing. Nor must users manually generate embeddings for ordinary NL2SQL requests. Embeddings become relevant to RAG
/vector workflows but are not a prerequisite for basic Select AI SQL generation.
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - prompt augmentation, LLM interaction, NL2SQL, and natural-language answers.
質問 # 26
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試験を受けることでOracle認定を取得することを期待する人が増えています。ただし、多くの人にとって試験は非常に困難です。特に正しい学習教材を選択せずに適切な方法を見つけた場合、1z0-1195-26試験に合格して関連する認定を取得することはより困難になります。関連する認定を効率的な方法で取得したい場合は、当社の1z0-1195-26学習教材を選択してください。弊社の1z0-1195-26学習教材が試験に合格し、簡単に認定を取得するのに役立ちます。
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