1z0-1195-26 PrüfungGuide, Oracle 1z0-1195-26 Zertifikat - Oracle AI Database Foundations Associate

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Oracle 1z0-1195-26 Exam Syllabus Topics:

SectionWeightObjectives
Implementing Select AI and AI Vector Search in Autonomous AI Database20%- 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
Building Low-Code Applications and Agentic AI10%- Describe Oracle APEX as Oracle's low-code platform
- Choose the appropriate Agent Factory capability for a no-code AI agent use case
Working with AI and Vector Foundations15%- 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
Using Oracle Database Actions and Data Studio Tools15%- 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 Database20%- Distinguish when graph capabilities and Property Graph Views fit a business use case
- Explain JSON and Oracle AI Database JSON capabilities
- Describe core graph concepts and graph analytic capabilities
Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics20%- Describe Autonomous AI Database characteristics, offerings, and deployment choices
- Create an Autonomous AI Database Serverless instance for a basic workload
- Explain modern data characteristics and the Oracle AI Database 26ai converged strategy

>> 1z0-1195-26 Fragen Und Antworten <<

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Oracle AI Database Foundations Associate 1z0-1195-26 Prüfungsfragen mit Lösungen (Q40-Q45):

40. Frage
What happens after a user asks a business question with Select AI?

Antwort: C

Begründung:
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.


41. Frage
What does the Oracle VECTOR data type enable?

Antwort: C

Begründung:
The Oracle VECTOR data type provides native database storage for vector values used by AI and machine- learning workloads. Oracle AI Database 26ai represents vectors as ordered numerical values with defined dimensionality and element formats. This allows vector embeddings representing text, images, audio, documents, or other content to reside directly alongside conventional business data rather than requiring a separate specialized vector database.
Native vector storage is foundational to Oracle AI Vector Search. Once embeddings are stored in VECTOR columns, SQL can apply vector-distance functions, perform exact or approximate similarity searches, create vector indexes, and combine semantic rankings with relational, JSON, text, spatial, or graph predicates.
Oracle emphasizes that keeping vectors with business data reduces data movement, lowers architecture complexity, and permits similarity searches against current transactional information.
The VECTOR type does not provide APEX page design-that is an Oracle APEX function. It does not universally validate JSON schemas, nor does it automatically convert relational tables into graph structures.
Those are separate Oracle Database capabilities. Consequently, native storage of vector values precisely describes its core function, consistent with the uploaded question source.
Study Guide reference: Working with AI and Vector Foundations - VECTOR data type, vector embeddings, vector columns, and AI Vector Search.


42. Frage
Which graph analytics capability is commonly used to rank important vertices based on their relationships?

Antwort: C

Begründung:
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


43. Frage
Which set of tools is specifically highlighted as available from Database Actions?

Antwort: A

Begründung:
The Database Actions environment specifically includes SQL, Data Modeler, REST, JSON, Oracle Machine Learning, and Oracle APEX , making option C correct. The uploaded assessment identifies the same tool set. Oracle's current Autonomous AI Database documentation describes Database Actions as a web- based interface for development, data tooling, administration, and monitoring. Its Development area includes SQL, Data Modeler, REST, JSON, Charts, Scheduling, Oracle Machine Learning, Spatial Studio, Graph Studio, and Oracle APEX.
SQL provides the browser-based worksheet for SQL and PL/SQL execution. Data Modeler supports database modeling and diagramming. REST provides tools for database REST services and APIs. JSON provides facilities for working with JSON collections and documents. Oracle Machine Learning exposes integrated ML development capabilities, while APEX launches Oracle's low-code application-development environment.
Oracle's Machine Learning documentation likewise identifies Database Actions as the entry point for Oracle Machine Learning.
The remaining choices largely describe OCI infrastructure or security-management services rather than Database Actions development tools. Compute, block storage, load balancing, IAM, billing, private endpoints, and Vault are managed elsewhere in OCI.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Database Actions Launchpad and Development tools.


44. Frage
A developer wants one place to work with SQL, REST endpoints, JSON features, APEX, and machine learning tools. Which entry point should the developer use?

Antwort: A

Begründung:
Database Actions is the correct centralized entry point because it is the browser-based development and administration environment bundled with Autonomous AI Database. Oracle documents Database Actions as providing development, data, administration, monitoring, and download capabilities. Its Development area includes SQL, Data Modeler, REST, JSON, Oracle Machine Learning, Graph Studio, and Oracle APEX, which directly matches the developer's requirement for a single integrated workspace. The SQL worksheet supports SQL and PL/SQL development, while REST and JSON tools expose database data through modern application interfaces. APEX provides low-code application development, and Oracle Machine Learning integrates analytical and machine-learning workflows with database-resident data.
The other choices are individual configuration concepts rather than comprehensive developer entry points.
Customer-managed key rotation relates to encryption-key lifecycle management; vector target accuracy controls approximate vector-search behavior; and a Property Graph View is a graph modeling construct.
Therefore, none provides the breadth of tooling required. The uploaded question set likewise identifies Database Actions as the correct response.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Database Actions development tools and integrated workspaces.


45. Frage
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