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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Describe Select AI in Autonomous AI Database - Apply AI Vector Search to combined semantic and business-data search scenarios - Determine how AI Vector Search supports GenAI pipelines and RAG |
| Topic 2: Working with AI and Vector Foundations | 15% | - Describe AI, AGI, and machine learning foundations - Apply vector distance and indexing concepts to similarity search needs - Explain vectors, embeddings, and the Oracle VECTOR data type |
| Topic 3: 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 4: Working with JSON and Graph in Oracle AI Database | 20% | - Explain JSON and Oracle AI Database JSON capabilities - Describe core graph concepts and graph analytic capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case |
| Topic 5: 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 6: 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 |
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質問 # 30
What describes the role of the Database Actions menu in Autonomous AI Database?
正解：D
解説：
Database Actions serves as the primary web-based launchpad for built-in Autonomous AI Database tooling.
Oracle describes Database Actions, also known as SQL Developer Web, as an integrated interface containing development, data, administration, monitoring, and download features. From this environment, users can access SQL Worksheet, Data Modeler, REST, JSON tools, Oracle Machine Learning, Graph Studio, Oracle APEX, Data Studio functions, database-user administration, Data Pump, Performance Hub, and other database services.
Its role is therefore broader than any single database technology. Graph Studio is one tool accessible through the environment rather than a replacement for SQL. Network isolation is configured through Autonomous AI Database networking facilities such as private endpoints, not by treating Database Actions itself as a network- access mechanism. Likewise, vector-index settings represent only one narrow area of database functionality and do not define the Database Actions environment. Oracle also documents Data Studio as a feature area accessed through Database Actions, reinforcing the concept of Database Actions as a common launchpad for multiple specialized workspaces. The uploaded question set marks the central built-in-tools location as the correct response.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Database Actions Launchpad, development tools, Data Studio, administration, and monitoring.
質問 # 31
A business wants one data platform where semantic similarity, relational consistency, and SQL-based filtering all work together.
Which statement aligns with this design goal?
正解：A
解説：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Oracle AI Database's converged architecture is designed to keep vectors and conventional business data in the same database so semantic ranking can be combined directly with SQL predicates. Oracle's VECTOR data type enables vector similarity search inside the database, and Oracle explicitly documents combining business- data searches with AI vector similarity search using SQL and the broader converged engine. This preserves transactional consistency and avoids exporting data to a separate vector platform merely to perform semantic retrieval. Options A and C incorrectly separate relational filtering from vector retrieval, while option B incorrectly requires graph modeling. The intended architecture is therefore one database that combines relational filtering, consistency, and vector similarity. This is a core design principle under "Implementing Select AI and AI Vector Search in Autonomous AI Database." Oracle Docs
質問 # 32
Which set is included in the Oracle AI Database 26ai converged approach?
正解：B
解説：
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.
質問 # 33
Which pair correctly matches an AI domain to an example?
正解：A
解説：
Vision - image classification is the correctly matched AI domain and use case. The uploaded source explicitly identifies option A as correct. Oracle Cloud Infrastructure Vision documentation confirms that Vision performs image analysis and includes image-classification capabilities for identifying objects and scene-based characteristics in images.
The distinction among the answer choices is based on the type of input being analyzed and the objective of the AI model. Computer vision works with images and visual content; classification assigns labels or categories based on visual characteristics. Language capabilities operate primarily on natural-language text and support functions such as entity recognition, sentiment analysis, text classification, and key-phrase extraction. Therefore, object detection in photographs belongs to vision rather than language.
Likewise, forecasting predicts future numerical or temporal outcomes from historical patterns; product- demand prediction is a typical forecasting scenario. Speech focuses on spoken audio, such as transcription or speech recognition, rather than business-demand prediction.
Option A is therefore the only domain/example relationship that is semantically and technically aligned.
Oracle Vision explicitly supports image classification, making the mapping unambiguous.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - AI domains, vision, language, speech, forecasting, and practical AI use cases.
質問 # 34
What does Select AI enable in Autonomous AI Database?
正解：A
解説：
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.
質問 # 35
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