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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Determine how AI Vector Search supports GenAI pipelines and RAG - Describe Select AI in Autonomous AI Database - Apply AI Vector Search to combined semantic and business-data search scenarios |
| Topic 2: Working with JSON and Graph in Oracle AI Database | 20% | - Describe core graph concepts and graph analytic capabilities - Explain JSON and Oracle AI Database JSON capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case |
| Topic 3: 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 4: 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 5: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Create an Autonomous AI Database Serverless instance for a basic workload |
| Topic 6: 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 |
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NEW QUESTION # 39
Which JSON feature helps represent repeating child data inside one document?
Answer: A
Explanation:
JSON arrays and nested objects provide the hierarchical structure required to represent repeating or composite child information within a single JSON document. The uploaded question set explicitly identifies this answer. Oracle AI Database supports standard JSON value types including objects and arrays. An object contains named property/value members, while an array contains an ordered sequence of JSON values.
Because array elements can themselves be objects or additional arrays, applications can represent complex parent-child structures naturally within one document.
For example, a customer document can contain an addresses array with multiple address objects, or an order can contain an items array where every element contains product, quantity, and price properties. This avoids artificially flattening inherently hierarchical information.
A scalar property is appropriate for a single value and therefore cannot naturally represent repeated child records. Merely storing an external identifier does not embed the child information in the document. Creating a separate standalone JSON document for every child would also fail the requirement to represent the repeating data inside one document .
Study Guide reference: Working with JSON and Graph in Oracle AI Database - JSON objects, arrays, hierarchical documents, and nested JSON structures.
NEW QUESTION # 40
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: D
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 # 41
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
Answer: C
NEW QUESTION # 42
How can developers access JSON Duality Views?
Answer: A
Explanation:
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.
NEW QUESTION # 43
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?
Answer: C
Explanation:
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.
NEW QUESTION # 44
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