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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Create an Autonomous AI Database Serverless instance for a basic workload |
| Working with JSON and Graph in Oracle AI Database | 20% | - Explain JSON and Oracle AI Database JSON capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case - Describe core graph concepts and graph analytic capabilities |
| 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 |
| Working with AI and Vector Foundations | 15% | - 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 |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Describe Select AI 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 |
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質問 # 32
A bank wants to identify indirect money movement patterns across several accounts to support fraud investigation. Which approach fits this requirement?
正解：D
解説：
Modeling accounts as vertices and transfers as edges is the appropriate graph design because the investigation depends on discovering relationships and multi-hop transaction paths. The uploaded source explicitly designates this model as correct. Oracle Property Graph documentation confirms that graphs represent linked information using vertices, edges, and associated properties and are suitable for pattern matching, path finding, community detection, and other relationship-centric analytics.
In this banking scenario, each account can become a vertex containing account attributes, while each transfer becomes a directed edge containing properties such as amount, timestamp, or transaction type. Graph traversal can then follow transfers across multiple intermediary accounts and reveal patterns that are difficult to identify from isolated transaction rows.
This is particularly valuable for fraud investigation because suspicious activity may involve chains, rings, circular movements, intermediaries, or highly connected accounts rather than one anomalous transaction.
Keyword searching transaction descriptions does not analyze relationships. Summary balances discard essential connectivity information, while one-transaction-at-a-time reporting prevents efficient multi-hop analysis.
The technical requirement is therefore fundamentally graph-oriented: determine how entities are connected and analyze paths among those entities.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - property graphs, vertices, edges, path analysis, and fraud-oriented relationship analysis.
質問 # 33
A team already uses Database Actions and now needs the part of the environment focused on discovery, load, integration, and cataloging. Which workspace should they open?
正解：B
解説：
Data Studio is the Database Actions workspace specifically designed for data-oriented activities such as loading, discovering, cataloging, transforming, integrating, analyzing, and preparing data. The uploaded source marks Data Studio as the correct option. Oracle's current Autonomous AI Database documentation states that Data Studio enables users to load, discover, catalog, transform, analyze, share, enrich, and automate data workflows through a web-based interface.
Data Studio contains purpose-built tools including Data Load , Catalog , Data Transforms , Data Analysis
, and Data Insights . Catalog provides a central mechanism for browsing, searching, discovering, inspecting, and acting on local or connected data assets, while Data Transforms supports graphical data integration and transformation workflows.
The alternatives perform different functions. Database Users handles user administration. Graph Studio focuses on graph modeling and analytics. SQL Worksheet provides interactive execution of SQL and PL/SQL rather than an integrated discovery, cataloging, and loading environment.
Therefore, when the question combines the terms discovery, load, integration, and cataloging , Data Studio is the definitive Oracle workspace.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Data Studio, Data Load, Catalog, and Data Transforms.
質問 # 34
Modern applications often need to work with relational data, JSON documents, graph relationships, and vector embeddings. What challenge does using a different specialized database for each need create?
正解：B
解説：
Using a different point-solution database for each data model can create data silos , increasing integration, synchronization, governance, and operational complexity. The uploaded assessment identifies option D as correct. Oracle AI Database 26ai is explicitly positioned as a converged database platform supporting AI, graph, document, spatial, relational, and other application models within one database architecture.
The problem with separate specialized stores is that an application may need to duplicate relational records into a document database, copy embeddings into a vector database, and maintain relationships in a graph database. These copies must remain synchronized as source data changes. Security controls, backups, monitoring, patching, access policies, and application integrations may also differ across platforms.
Oracle's converged model instead allows different representations and workloads to operate on centrally governed data. Oracle specifically notes that the converged platform provides synergy among multiple data models and enables different types of information to be joined and manipulated together.
Therefore, separate databases do not automatically share security or eliminate transformation. Those are precisely the architectural burdens that convergence is intended to reduce.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - converged database strategy and elimination of data silos.
質問 # 35
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
正解：A
質問 # 36
A data engineer is creating a vector similarity query and wants to choose the distance metric correctly.
Which guidance should be applied?
正解：C
解説：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Oracle recommends using the distance metric associated with the embedding model that generated the vectors. Different metrics-such as cosine, Euclidean, dot product, Manhattan, or Hamming-measure similarity in different ways, and an embedding model is normally trained or intended to be evaluated with a particular metric. Oracle's AI Vector Search documentation states that it is generally best to match the query distance metric to the metric used to train the embedding model. Oracle also notes that a vector index should be created and searched with the appropriate distance function; using a different function can prevent index use and trigger exact search behavior. Table row count, maintenance schedules, and the presence of JSON attributes do not determine semantic vector geometry. Therefore, option C is the correct guidance. Oracle Docs
質問 # 37
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