1z0-1195-26 Zertifikatsfragen & 1z0-1195-26 Zertifikatsdemo

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Oracle 1z0-1195-26 Exam Syllabus Topics:

Section	Weight	Objectives
Oracle Database Services — Exadata, DBCS, and Engineered Systems	~11%	- Exadata architecture and features - Database Cloud Service (DBCS) characteristics
Converged Database — Multi-Model and AI Capabilities	~15%	- JSON, Graph, Spatial, and key-value data support - Oracle AI Vector Search concepts - Select AI and natural language querying
Security, Resilience, and Cloud Integration	~21%	- Cloud-native database services and deployment strategies - Database security architectures and data protection - High availability, backup, and disaster recovery
Data Management and Oracle Data Platform Overview	~11%	- Modern data platform value and architecture - Oracle Data Strategy and multi-cloud deployment models - Data management concepts and data types
Autonomous AI Database and Tools	~16%	- Core features of Autonomous AI Database - Shared vs dedicated infrastructure - Built-in management and query tools
Oracle Machine Learning and AI Integration	~15%	- In-database machine learning algorithms - AI agents and LLM integration - Oracle Data Studio and visualization
MySQL HeatWave and NoSQL Services	~11%	- Oracle NoSQL Database features and use cases - MySQL HeatWave architecture and analytics

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1z0-1195-26 Zertifikatsdemo, 1z0-1195-26 Praxisprüfung

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Oracle AI Database Foundations Associate 1z0-1195-26 Prüfungsfragen mit Lösungen (Q37-Q42):

37. Frage
What does Select AI enable in Autonomous AI Database?

A. Administrators can provision database instances from natural-language prompts
B. Users can replace every SQL query with graph pattern matching
C. Administrators can rotate customer-managed encryption keys from a query prompt
D. Users can ask natural-language questions to get data answers

Antwort: D

Begründung:
Select AI enables users to interact with Autonomous AI Database by expressing requests in natural language rather than manually constructing SQL. Oracle documents that Select AI uses generative AI and large language models to convert natural-language input into Oracle SQL. Depending on the requested action, the generated SQL can be displayed, explained, executed, or its results can be transformed into a natural-language response. This makes database information accessible to users who understand the business question but may not know SQL syntax or the underlying schema.
Internally, Select AI can augment a prompt with schema metadata, interact with the configured LLM, generate SQL, run the query, and optionally narrate the resulting data. It also extends beyond NL2SQL into Retrieval-Augmented Generation, conversations, and other generative-AI capabilities. It does not replace relational SQL with graph pattern matching, manage encryption-key rotation, or provision Autonomous AI Database infrastructure. Those are unrelated database administration or graph functions. The supplied question bank explicitly marks the natural-language data-query capability as correct.
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - Select AI, natural-language interaction, NL2SQL, and LLM integration.

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

A. Native storage of vector values in the database
B. Automatic conversion of every table into a graph model
C. Automatic JSON schema validation for all APIs
D. Browser-based low-code page design

Antwort: A

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.

39. Frage
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?

A. Start with a pre-built agent or template, then use Agent Builder if more customization is needed
B. Build a custom agent from a blank setup before reviewing any available templates
C. Create a prompt-only prototype and publish it without using Agent Factory agent options
D. Configure data-source connections before selecting a template or agent starting point

Antwort: A

Begründung:
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.

40. Frage
A development team wants to receive patches before the regular maintenance schedule so they can validate changes early.
Which maintenance option should they select?

A. Early maintenance
B. Application-controlled maintenance
C. Late maintenance
D. Regular maintenance

Antwort: A

Begründung:
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

41. Frage
In a property graph, what are vertices and edges?

A. Vertices are path lengths and edges are SQL functions
B. Vertices are storage partitions, and edges are encryption keys
C. Vertices represent entities, and edges represent relationships between them
D. Vertices are JSON collections, and edges are maintenance policies

Antwort: C

42. Frage
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