ExamPassdump는 여러분을 성공으로 가는 길에 도움을 드리는 사이트입니다. ExamPassdump에서는 여러분이 안전하게 간단하게Oracle인증1z0-1195-26시험을 패스할 수 있는 자료들을 제공함으로 빠른 시일 내에 IT관련지식을 터득하고 한번에 시험을 패스하실 수 있습니다.
| Section | Weight | Objectives |
|---|---|---|
| Security, Resilience, and Cloud Integration | ~21% | - Database security architectures and data protection - Cloud-native database services and deployment strategies - High availability, backup, and disaster recovery |
| 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 |
| Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Database Cloud Service (DBCS) characteristics - Exadata architecture and features |
| 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 |
| Autonomous AI Database and Tools | ~16% | - Built-in management and query tools - Shared vs dedicated infrastructure - Core features of Autonomous AI Database |
| Oracle Machine Learning and AI Integration | ~15% | - AI agents and LLM integration - In-database machine learning algorithms - Oracle Data Studio and visualization |
ExamPassdump 의 IT전문가들이 자신만의 경험과 끊임없는 노력으로 최고의 Oracle 1z0-1195-26학습자료를 작성해 여러분들이Oracle 1z0-1195-26시험에서 패스하도록 최선을 다하고 있습니다. 덤프는 최신 시험문제를 커버하고 있어 시험패스율이 높습니다. Oracle 1z0-1195-26시험을 보기로 결심한 분은 가장 안전하고 가장 최신인 적중율 100%에 달하는Oracle 1z0-1195-26시험대비덤프를 ExamPassdump에서 받을 수 있습니다.
질문 # 33
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 # 34
A support portal must search product manuals semantically while also filtering results by product line and support level stored in relational columns. How does Oracle AI Vector Search support this requirement?
정답：C
설명：
Oracle AI Vector Search supports semantic similarity search together with conventional relational predicates in SQL , making option C correct. The uploaded source explicitly identifies this integrated approach. Oracle AI Database 26ai provides the native VECTOR data type so embeddings can reside directly alongside relational business attributes. Oracle states that AI-powered vector similarity searches can be combined with business-data searches using SQL and the full capabilities of the converged database.
For the support portal, each manual or document chunk can have an embedding while relational columns identify its product line, entitlement level, version, or support tier. A SQL statement can restrict rows using predicates such as product line and support level while ordering eligible records by vector distance from the user's query embedding.
This approach is superior to retrieving a broad semantic result set and filtering it later in application code.
External post-filtering can waste processing and may remove highly ranked records without correctly replacing them with the next eligible matches.
Document APIs and property graphs are also not prerequisites. Oracle's converged architecture permits relational and vector criteria to operate together directly.
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - hybrid business filtering, vector similarity ranking, and integrated SQL.
질문 # 35
How can developers create vectors for data objects in Oracle AI Database 26ai?
정답：A
설명：
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.
질문 # 36
How can developers access JSON Duality Views?
정답：C
설명：
Developers can access JSON-Relational Duality Views using document-oriented interfaces, including Oracle AI Database API for MongoDB , as well as SQL-based database interfaces. The uploaded source explicitly identifies "MongoDB-compatible APIs or SQL" as correct. Oracle documentation confirms that duality views expose relational table data as JSON documents and that applications can interact with the same underlying data either document-centrically or relationally.
With the MongoDB-compatible API, the duality view can be treated as a document collection by applications using familiar MongoDB drivers and development patterns. At the same time, because the authoritative data remains in Oracle relational tables, SQL and other relational capabilities can operate directly on the same information. This is a primary architectural advantage of JSON-Relational Duality.
No export into a separate document store is necessary; doing so would reintroduce data duplication and synchronization problems that duality views are designed to avoid. Graph visualization is unrelated to document access, and although APEX applications can consume database data, APEX-specific PL/SQL packages are not the principal interface defining duality-view access.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - JSON Duality Views, Oracle AI Database API for MongoDB, SQL access, and unified relational/document development.
질문 # 37
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?
정답：A
설명：
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.
질문 # 38
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