Um keine Reue und Bedauern in Ihrem Leben zu hinterlassen, sollen Sie jede Gelegenheit ergreifen, um das Leben zu vebessern. Haben Sie das gemacht? Die Fragenkataloge zur Oracle 1z0-1195-26 Zertifizierungsprüfung von ExamFragen helfen den IT-Fachleuten, die Erfolg erzielen wollen, die Oracle 1z0-1195-26 Zertifizierungsprüfung zu bestehen. Um den Erfolg nicht zu verpassen, machen Sie doch schnell.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - 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 |
| Topic 2: 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 |
| Topic 3: Building Low-Code Applications and Agentic AI | 10% | - Choose the appropriate Agent Factory capability for a no-code AI agent use case - Describe Oracle APEX as Oracle's low-code platform |
| Topic 4: Working with JSON and Graph in Oracle AI Database | 20% | - Explain JSON and Oracle AI Database JSON capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case - Describe core graph concepts and graph analytic capabilities |
| Topic 5: 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 |
| Topic 6: Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Apply AI Vector Search to combined semantic and business-data search scenarios - Determine how AI Vector Search supports GenAI pipelines and RAG - Describe Select AI in Autonomous AI Database |
Solange Sie die Prüfung benötigen, können wir jederzeit die Schulungsunterlagen zur Oracle 1z0-1195-26 Zertifizierungsprüfung aktualisieren, um Ihre Prüfungsbedürfnisse abzudecken. Die Schulungsunterlagen von ExamFragen enthalten viele Übungsfragen und Antworten zur Oracle 1z0-1195-26 Zertifizierungsprüfung und geben Ihnen eine 100%-Pass-Garantie. Mit unseren Schulungsunterlagen können Sie sich besser auf Ihre 1z0-1195-26 Prüfung vorbereiten. Außerdem bieten wir Ihnen einen einjährigen kostenlosen Update-Service.
25. Frage
Which set is included in the Oracle AI Database 26ai converged approach?
Antwort: C
Begründung:
Oracle AI Database 26ai follows a converged database strategy in which multiple data models and workload capabilities are supported within a unified database platform. Oracle describes 26ai as supporting AI, microservices, graph, document, spatial, and relational applications within one converged database. AI Vector Search extends that approach by adding native vector storage and similarity search alongside established relational, JSON, graph, text, and spatial functionality.
The architectural objective is to avoid creating separate point-solution databases whenever an application needs a different representation of data. For example, structured customer information can remain relational, document-oriented information can use JSON, relationships can be analyzed as graphs, geospatial information can use Spatial capabilities, and semantic representations can reside as vectors. These capabilities can then participate in integrated queries instead of forcing applications to continuously replicate and synchronize data among independent database products.
Options A and C contradict this strategy by introducing separate document, graph, or vector stores. Option B mixes workload capabilities with infrastructure and operational concepts such as storage buckets and billing rather than identifying the database's core converged data models. Option D correctly enumerates relational, JSON, vector, graph, and spatial capabilities and matches the source question's designated answer.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - converged database architecture and multi-model data support.
26. Frage
In a RAG-style application, where does similarity search apply?
Antwort: B
Begründung:
Similarity search performs the retrieval component of Retrieval-Augmented Generation. Source documents or other content are converted into vector embeddings and stored in a vector-capable database. The user's question is similarly transformed into an embedding, and a vector-distance or similarity calculation identifies stored embeddings whose semantic meaning is closest to the query. Oracle describes semantic similarity search as identifying and retrieving data points that closely match a query by comparing feature vectors in the vector store.
The resulting documents or chunks provide relevant contextual information to the generative model. Select AI with RAG, for example, retrieves content from a configured vector store through semantic similarity search and places that content into an augmented prompt sent to the LLM. The LLM then generates a response grounded in retrieved enterprise information. Similarity search therefore does not create the original documents, perform authentication, or eliminate response generation. Returning raw vectors alone would also fail to achieve RAG's purpose because the retrieved source content must ultimately inform the generated response. The uploaded question set confirms the retrieval of relevant stored content as the correct function.
Study Guide reference: Working with AI and Vector Foundations - semantic similarity search, vector retrieval, embeddings, and RAG architecture.
27. Frage
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
Antwort: B
Begründung:
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.
28. Frage
How can developers create vectors for data objects in Oracle AI Database 26ai?
Antwort: B
Begründung:
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.
29. Frage
What does Oracle mean by a converged database strategy?
Antwort: A
Begründung:
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
A converged database strategy means using one database engine to support multiple modern data models and workload types rather than deploying a separate specialized database for each requirement. Oracle AI Database provides native support for relational, JSON/document, vector, graph, spatial, text, and other data, while supporting transactional, analytic, AI Vector Search, and mixed workloads. This reduces data movement, synchronization, security fragmentation, and operational complexity. A converged database does not mean one reporting tool replaces every access language, one application server manages unrelated databases, or one storage tier is reserved only for AI. Those choices confuse application tooling or storage design with the database architecture itself. Option D accurately expresses Oracle's converged strategy:
multiple data types and workloads supported together in a unified database platform. Oracle
30. Frage
......
Gott will, dass ich eine Person mit Fähigkeit, statt eine gute aussehende Puppe zu werden. Wenn ich IT-Branche wähle, habe ich dem Gott meine Fähigkeiten bewiesen. Aber der Gott ist mit nichts zufrieden. Er hat mich gezwungen, nach oben zu gehen. Die Oracle 1z0-1195-26 Zertifizierungsprüfung ist eine große Herausforderung in meinem Leben. So habe ich sehr hart gelernt. Aber das macht doch nichts, weil ich ExamFragen die Fragenkataloge zur Oracle 1z0-1195-26 Zertifizierung gekauft habe. Mit ihr kann ich sicher die die Oracle 1z0-1195-26 Prüfung bestehen. Der Weg ist unter unseren Füßen, nur Sie können ihre Richtung entscheiden. Mit den Prüfungsmaterialien zur Oracle 1z0-1195-26 Prüfung von ExamFragen können Sie sicher eine bessere Zukunft haben.
1z0-1195-26 Testfagen: https://www.examfragen.de/1z0-1195-26-pruefung-fragen.html