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| Section | Objectives |
|---|---|
| Building Low-Code Applications and Agentic AI | - 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 | - Describe Database Actions and core development tools - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | - Determine how AI Vector Search supports GenAI pipelines and RAG - Apply AI Vector Search to combined semantic and business-data search scenarios - Describe Select AI in Autonomous AI Database |
| Working with JSON and Graph in Oracle AI Database | - Describe core graph concepts and graph analytic capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case - Explain JSON and Oracle AI Database JSON capabilities |
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | - Create an Autonomous AI Database Serverless instance for a basic workload - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Describe Autonomous AI Database characteristics, offerings, and deployment choices |
| Working with AI and Vector Foundations | - Describe AI, AGI, and machine learning foundations - Explain vectors, embeddings, and the Oracle VECTOR data type - Apply vector distance and indexing concepts to similarity search needs |
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NEW QUESTION # 15
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?
Answer: A
Explanation:
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.
NEW QUESTION # 16
Which example is a common AI use case?
Answer: C
Explanation:
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Fraud detection is a standard AI use case because machine-learning and anomaly-detection models can identify unusual behavioral patterns across transaction attributes such as amount, account, merchant, location, and historical activity. Oracle documents AI-driven fraud-prevention architectures that analyze transaction behavior and flag suspicious or anomalous activity for investigation. The other options are administrative database or network tasks rather than AI inference problems. Alphabetically listing tables is ordinary metadata browsing; rotating an encryption key is deterministic security administration; and assigning a subnet CIDR is infrastructure configuration. None requires a model to learn patterns from data. Detecting fraudulent transactions from behavior patterns therefore best represents an AI workload and aligns with the Oracle AI Database foundations coverage of practical AI domains and use cases. Oracle Docs
NEW QUESTION # 17
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?
Answer: B
Explanation:
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.
NEW QUESTION # 18
A development team wants to receive patches before the regular maintenance schedule so they can validate changes early.
Which maintenance option should they select?
Answer: D
Explanation:
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
NEW QUESTION # 19
What does a JSON Duality View enable?
Answer: C
Explanation:
A JSON-Relational Duality View allows applications to work with relationally stored information as JSON documents without maintaining a separate document-store copy. The uploaded source identifies this exact capability as the correct answer. Oracle AI Database documentation confirms that a duality view maps relational table data to hierarchical JSON documents that are materialized on demand rather than separately stored. Applications can therefore access and, when permitted, modify the same underlying information either through relational tables or through its document representation.
This architecture preserves relational advantages such as normalization, integrity constraints, SQL processing, and transactional consistency while giving document-oriented applications a natural JSON interface. A change made through an updatable JSON document is reflected in the underlying relational data, and relational changes are correspondingly visible through the duality view.
The feature does not eliminate SQL, transform property graphs into vector indexes, or require synchronization with an independent document database. Oracle specifically positions JSON-Relational Duality as a mechanism for combining relational and document development models around one authoritative data representation.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - JSON-Relational Duality Views, relational storage, and document-oriented access.
NEW QUESTION # 20
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