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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security, Resilience, and Cloud Integration | ~21% | - Cloud-native database services and deployment strategies - High availability, backup, and disaster recovery - Database security architectures and data protection |
| Topic 2: MySQL HeatWave and NoSQL Services | ~11% | - Oracle NoSQL Database features and use cases - MySQL HeatWave architecture and analytics |
| Topic 3: Data Management and Oracle Data Platform Overview | ~11% | - Data management concepts and data types - Modern data platform value and architecture - Oracle Data Strategy and multi-cloud deployment models |
| Topic 4: Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Database Cloud Service (DBCS) characteristics - Exadata architecture and features |
| Topic 5: Oracle Machine Learning and AI Integration | ~15% | - Oracle Data Studio and visualization - AI agents and LLM integration - In-database machine learning algorithms |
| Topic 6: Converged Database — Multi-Model and AI Capabilities | ~15% | - Select AI and natural language querying - Oracle AI Vector Search concepts - JSON, Graph, Spatial, and key-value data support |
| Topic 7: Autonomous AI Database and Tools | ~16% | - Built-in management and query tools - Core features of Autonomous AI Database - Shared vs dedicated infrastructure |
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질문 # 17
A project team wants a built-in workspace to analyze data assets and support sharing or collaboration after preparation work is complete.
Which choice fits that requirement?
정답：D
설명：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Data Studio is the correct choice because Oracle positions it as the integrated, web-based data workspace inside Database Actions for loading, discovering, cataloging, transforming, analyzing, sharing, enriching, and automating data workflows. Its Data Analysis capability supports reports and visual analysis, while Data Share and Data Marketplace support governed distribution and collaboration after data preparation. SQL Worksheet is primarily for SQL and PL/SQL execution; Database Users is for user administration; and Private Endpoint configuration is a networking control, not a data-analysis workspace. Therefore, a team that needs to continue from prepared data into analysis and sharing should use Data Studio. This maps directly to the Oracle AI Database topic "Using Oracle Database Actions and Data Studio Tools." Oracle Docs
질문 # 18
In a property graph, what are vertices and edges?
정답：D
설명：
A property graph models information as vertices representing entities and edges representing relationships among those entities. Oracle's Property Graph documentation defines a graph as a collection of objects or vertices connected by arrows or edges. Both vertices and edges can contain properties expressed as key-value pairs. This model is particularly effective when understanding relationships is as important as examining individual records.
For example, in a banking model, customer accounts can be vertices while transfers between accounts become edges. In a social network, people are vertices and relationships such as "knows," "follows," or "works with" become edges. Each edge identifies source and destination vertices and can carry a label and additional properties. Oracle AI Database can then perform graph pattern matching and analytics over these relationships.
Path lengths are results or attributes of graph traversal rather than vertices themselves. SQL functions are query constructs, not graph edges. Likewise, database partitions, encryption keys, JSON collections, and maintenance policies do not define the fundamental property-graph data model. The uploaded assessment explicitly identifies entities and relationships as the proper interpretation.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - property graphs, vertices, edges, properties, and relationship analysis.
질문 # 19
Which set of tools is specifically highlighted as available from Database Actions?
정답：B
설명：
The Database Actions environment specifically includes SQL, Data Modeler, REST, JSON, Oracle Machine Learning, and Oracle APEX , making option C correct. The uploaded assessment identifies the same tool set. Oracle's current Autonomous AI Database documentation describes Database Actions as a web- based interface for development, data tooling, administration, and monitoring. Its Development area includes SQL, Data Modeler, REST, JSON, Charts, Scheduling, Oracle Machine Learning, Spatial Studio, Graph Studio, and Oracle APEX.
SQL provides the browser-based worksheet for SQL and PL/SQL execution. Data Modeler supports database modeling and diagramming. REST provides tools for database REST services and APIs. JSON provides facilities for working with JSON collections and documents. Oracle Machine Learning exposes integrated ML development capabilities, while APEX launches Oracle's low-code application-development environment.
Oracle's Machine Learning documentation likewise identifies Database Actions as the entry point for Oracle Machine Learning.
The remaining choices largely describe OCI infrastructure or security-management services rather than Database Actions development tools. Compute, block storage, load balancing, IAM, billing, private endpoints, and Vault are managed elsewhere in OCI.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Database Actions Launchpad and Development tools.
질문 # 20
What is the main difference between Autonomous AI Database Serverless and Dedicated deployment choices?
정답：B
설명：
The fundamental distinction is that Serverless emphasizes simplicity and elasticity , whereas Dedicated provides isolated infrastructure and greater operational customization . This is the answer explicitly identified in the uploaded question set. Oracle documentation describes the Serverless model as ultra-simple and elastic: customers manage the Autonomous AI Database while Oracle manages the underlying Exadata infrastructure. Dedicated, by contrast, provides exclusive compute, storage, network, and database resources.
Oracle also characterizes Dedicated as a private-cloud-in-public-cloud deployment model with high levels of security isolation and governance. Dedicated environments can support customizable operational policies involving workload placement, update scheduling, availability, capacity usage, and other infrastructure-level concerns. Serverless removes much of that infrastructure planning and is therefore well suited to organizations prioritizing rapid provisioning and elastic consumption.
Neither deployment is restricted exclusively to JSON or relational workloads, and the distinction is not primarily about available developer SQL tools. Option A reverses the infrastructure characteristics: it is Dedicated-not Serverless-that supplies the isolated dedicated resource model.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Serverless versus Dedicated deployment architecture.
질문 # 21
When comparing a query vector with stored vectors, what does a smaller vector distance indicate?
정답：C
설명：
Vector distance quantifies how far apart two vector representations are according to a specified mathematical distance metric. In semantic search, embeddings place semantically related content near one another in a multidimensional vector space. Consequently, when a distance-oriented metric such as cosine distance or Euclidean distance produces a smaller value, the vectors are considered closer and therefore generally more similar according to the embedding model. Oracle's cosine-distance documentation explains the inverse relationship between cosine similarity and cosine distance: increasingly similar vector directions produce smaller cosine-distance values.
For example, cosine distance is calculated as 1 - cosine similarity. Identical or highly aligned semantic representations therefore approach a distance of zero, while increasingly different vectors produce larger distances. This principle is why Oracle SQL similarity searches commonly order rows by VECTOR_DISTANCE(...) in ascending order and fetch the first N rows: the rows with the smallest distances are the nearest semantic matches. Distance has no relationship to database maintenance windows, and a smaller value does not prove that different embedding models were used. Vector search also remains available through SQL and does not require GraphQL.
Study Guide reference: Working with AI and Vector Foundations - vector embeddings, distance metrics, semantic similarity, and top-K ranking.
질문 # 22
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