Oracle인증 1z0-1195-26시험패스 공부방법을 찾고 있다면 제일 먼저PassTIP를 추천해드리고 싶습니다. Oracle인증 1z0-1195-26시험이 많이 어렵다는것은 모두 알고 있는 것입니다. PassTIP에서 출시한 Oracle인증 1z0-1195-26덤프는 실제시험을 대비하여 연구제작된 멋진 작품으로서 Oracle인증 1z0-1195-26시험적중율이 최고입니다. Oracle인증 1z0-1195-26시험패스를 원하신다면PassTIP의 제품이 고객님의 소원을 들어줄것입니다.
| Section | Weight | Objectives |
|---|---|---|
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Create an Autonomous AI Database Serverless instance for a basic workload |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - 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 | 15% | - Apply vector distance and indexing concepts to similarity search needs - Explain vectors, embeddings, and the Oracle VECTOR data type - Describe AI, AGI, and machine learning foundations |
| Building Low-Code Applications and Agentic AI | 10% | - Describe Oracle APEX as Oracle's low-code platform - Choose the appropriate Agent Factory capability for a no-code AI agent use case |
| Using Oracle Database Actions and Data Studio Tools | 15% | - Describe Database Actions and core development tools - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks |
| Working with JSON and Graph in Oracle AI Database | 20% | - Distinguish when graph capabilities and Property Graph Views fit a business use case - Describe core graph concepts and graph analytic capabilities - Explain JSON and Oracle AI Database JSON capabilities |
Oracle 1z0-1195-26인증시험을 어떻게 준비하면 될가 아직도 고민하고 계시죠? 학원에 등록하자니 시간도 없고 돈도 많이 들고 쉽게 엄두가 나지 않는거죠? PassTIP제품을 구매하신다면 그런 부담을 이제 끝입니다. PassTIP덤프는 더욱 가까지 여러분들께 다가가기 위하여 그 어느 덤프판매 사이트보다 더욱 저렴한 가격으로 여러분들을 맞이하고 있습니다. Oracle 1z0-1195-26덤프는PassTIP제품이 최고랍니다.
질문 # 10
A business team wants to launch a no-code AI agent quickly. They prefer to start from a ready-made option and later refine the publishing workflow. Which Private Agent Factory capability path fits this need?
정답：C
설명：
Oracle AI Database Private Agent Factory is explicitly designed as a no-code environment for rapidly building, testing, and deploying intelligent agents. Oracle documents that Agent Factory supports pre-built agents, custom-built agents, and end-to-end workflows and includes curated agentic templates intended to accelerate implementation. Starting from one of these ready-made assets minimizes initial design work and is therefore the strongest match for a business team that prioritizes rapid deployment.
If additional customization becomes necessary, Agent Builder provides a visual no-code environment for constructing and refining agents and workflows from modular components. Oracle describes capabilities including drag-and-drop workflow construction, data connectors, LLM integration, APIs, custom agent creation, multi-agent orchestration, and reusable templates. This establishes a logical progression: begin with a pre-built agent/template to obtain functionality quickly, then move into Agent Builder when deeper customization or workflow tailoring is required. Starting from a completely blank agent would unnecessarily increase implementation effort, while a prompt-only prototype bypasses Agent Factory's governed agent capabilities. The source question likewise identifies the pre-built-to-Agent-Builder path as correct.
Study Guide reference: Building Low-Code Applications and Agentic AI - Private Agent Factory, pre-built agents, templates, and Agent Builder.
질문 # 11
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
정답：B
설명：
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.
질문 # 12
Which offering is positioned for JSON-centric workloads and includes Oracle Database API for MongoDB?
정답：A
설명：
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.
질문 # 13
When comparing a query vector with stored vectors, what does a smaller vector distance indicate?
정답：C
설명：
Vector distance quantifies how far apart two vector representations are according to a specified mathematical distance metric. In semantic search, embeddings place semantically related content near one another in a multidimensional vector space. Consequently, when a distance-oriented metric such as cosine distance or Euclidean distance produces a smaller value, the vectors are considered closer and therefore generally more similar according to the embedding model. Oracle's cosine-distance documentation explains the inverse relationship between cosine similarity and cosine distance: increasingly similar vector directions produce smaller cosine-distance values.
For example, cosine distance is calculated as 1 - cosine similarity. Identical or highly aligned semantic representations therefore approach a distance of zero, while increasingly different vectors produce larger distances. This principle is why Oracle SQL similarity searches commonly order rows by VECTOR_DISTANCE(...) in ascending order and fetch the first N rows: the rows with the smallest distances are the nearest semantic matches. Distance has no relationship to database maintenance windows, and a smaller value does not prove that different embedding models were used. Vector search also remains available through SQL and does not require GraphQL.
Study Guide reference: Working with AI and Vector Foundations - vector embeddings, distance metrics, semantic similarity, and top-K ranking.
질문 # 14
A development team needs an Autonomous AI Database deployment that starts small, minimizes setup effort, and can scale easily. Which deployment choice fits this requirement?
정답：B
설명：
Serverless best satisfies requirements for minimal infrastructure setup, a small starting footprint, and elastic scaling. The uploaded source identifies option D as correct. Oracle documentation describes the Serverless deployment model as ultra-simple and elastic , with Oracle managing the Exadata infrastructure underneath the Autonomous AI Database service.
This means application teams can focus principally on database-level resources and workloads rather than first designing and administering dedicated infrastructure capacity. Autonomous AI Database Serverless is therefore well aligned with teams seeking rapid provisioning and the ability to adjust resources as workload requirements change.
Dedicated deployment addresses a different requirement profile. Oracle describes Dedicated as providing exclusive compute, storage, network, and database resources, with stronger infrastructure isolation, operational control, governance, and customization. Those characteristics are valuable for organizations requiring dedicated Exadata resources or greater infrastructure control, but they do not minimize initial capacity planning and infrastructure considerations in the way Serverless does.
A fixed-capacity alternative also contradicts the requirement for easy scaling. Therefore, the exam distinction is straightforward: Serverless emphasizes simplicity and elasticity; Dedicated emphasizes isolation and greater infrastructure control.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Serverless versus Dedicated deployment models.
질문 # 15
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Oracle인증 1z0-1195-26시험을 어떻게 공부하면 패스할수 있을지 고민중이시면 근심걱정 버리시고PassTIP 의 Oracle인증 1z0-1195-26덤프로 가보세요. 문항수가 적고 적중율이 높은 세련된Oracle인증 1z0-1195-26시험준비 공부자료는PassTIP제품이 최고입니다.
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