Oracle인증 1z0-1195-26시험은 등록하였는데 시험준비는 아직이라구요? Oracle인증 1z0-1195-26시험일이 다가오고 있는데 공부를 하지 않아 두려워 하고 계시는 분들은 이 글을 보는 순간 시험패스에 자신을 가지게 될것입니다. 시험준비 시간이 적다고 하여 패스할수 없는건 아닙니다. Pass4Test의Oracle인증 1z0-1195-26덤프와의 근사한 만남이Oracle인증 1z0-1195-26패스에 화이팅을 불러드립니다. 덤프에 있는 문제만 공부하면 되기에 시험일이 며칠뒤라도 시험패스는 문제없습니다. 더는 공부하지 않은 자신을 원망하지 마시고 결단성있게Pass4Test의Oracle인증 1z0-1195-26덤프로 시험패스에 고고싱하세요.
| Section | Objectives |
|---|---|
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | - Create an Autonomous AI Database Serverless instance for a basic workload - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy |
| Working with JSON and Graph in Oracle AI Database | - Describe core graph concepts and graph analytic capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case - Explain JSON and Oracle AI Database JSON capabilities |
| Using Oracle Database Actions and Data Studio Tools | - 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 | - Describe Select AI in Autonomous AI Database - Apply AI Vector Search to combined semantic and business-data search scenarios - Determine how AI Vector Search supports GenAI pipelines and RAG |
| Working with AI and Vector Foundations | - Describe AI, AGI, and machine learning foundations - Apply vector distance and indexing concepts to similarity search needs - Explain vectors, embeddings, and the Oracle VECTOR data type |
| Building Low-Code Applications and Agentic AI | - Describe Oracle APEX as Oracle's low-code platform - Choose the appropriate Agent Factory capability for a no-code AI agent use case |
>> Oracle 1z0-1195-26인증덤프 샘플 다운로드 <<
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질문 # 53
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?
정답：B
설명：
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.
질문 # 54
Which graph analytics capability is commonly used to rank important vertices based on their relationships?
정답：B
설명：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
PageRank is the graph-analytics algorithm intended to measure the relative importance of vertices based on graph relationships. Oracle's property-graph documentation describes PageRank as ranking vertices by considering incoming neighbors and the importance of those neighbors. This makes it appropriate for identifying influential or significant entities in connected data, such as important web pages, accounts, people, devices, or other nodes. Private endpoint access is a networking feature, JSON Duality View is a relational-to- JSON representation mechanism, and a vector distance metric measures similarity between vector embeddings. None of those performs graph centrality ranking. Therefore, PageRank is the only option that directly satisfies the requirement to rank vertices according to their relationships. This belongs under
"Working with JSON and Graph in Oracle AI Database." Oracle Docs
질문 # 55
Which set of characteristics is commonly used to describe Autonomous AI Database?
정답：C
설명：
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.
질문 # 56
Which pair correctly matches an AI domain to an example?
정답：C
설명：
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.
질문 # 57
A development team wants to receive patches before the regular maintenance schedule so they can validate changes early.
Which maintenance option should they select?
정답：B
설명：
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
질문 # 58
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