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| Section | Weight | Objectives |
|---|---|---|
| MySQL HeatWave and NoSQL Services | ~11% | - MySQL HeatWave architecture and analytics - Oracle NoSQL Database features and use cases |
| Security, Resilience, and Cloud Integration | ~21% | - Database security architectures and data protection - High availability, backup, and disaster recovery - Cloud-native database services and deployment strategies |
| Autonomous AI Database and Tools | ~16% | - Shared vs dedicated infrastructure - Built-in management and query tools - Core features of Autonomous AI Database |
| Oracle Machine Learning and AI Integration | ~15% | - Oracle Data Studio and visualization - AI agents and LLM integration - In-database machine learning algorithms |
| 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 |
| Data Management and Oracle Data Platform Overview | ~11% | - Oracle Data Strategy and multi-cloud deployment models - Data management concepts and data types - Modern data platform value and architecture |
| Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Database Cloud Service (DBCS) characteristics - Exadata architecture and features |
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36. Frage
What does Select AI enable in Autonomous AI Database?
Antwort: B
Begründung:
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.
37. Frage
In a property graph, what are vertices and edges?
Antwort: B
38. Frage
A data analyst wants to discover data, load it into the platform, and prepare it for later use without leaving the built-in tools. Which workspace fits this task?
Antwort: D
Begründung:
Data Studio is the correct built-in workspace because its purpose directly covers data discovery, loading, preparation, transformation, analysis, cataloging, sharing, and related self-service workflows. The uploaded source designates Data Studio as the correct answer. Oracle's current Autonomous AI Database documentation states that Data Studio can be used to load, discover, catalog, transform, analyze, share, enrich, and automate data workflows through a web-based interface.
Data Studio is available through Database Actions and contains specialized tools including Data Load , Catalog , Data Transforms , Data Analysis , and Data Insights . Data Load can ingest or link data from local files, remote databases, cloud storage, and live feeds, while Catalog supports searching, discovering, inspecting, and managing available data assets.
OCI Vault addresses secrets and encryption-key management rather than data preparation.
VECTOR_DISTANCE is an individual SQL function used for vector comparisons, not an integrated analyst workspace. A Property Graph View defines a graph representation over data but likewise does not provide general discovery and loading functionality.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Data Studio, Data Load, Catalog, Data Transforms, and self-service data preparation.
39. Frage
Which set of characteristics is commonly used to describe Autonomous AI Database?
Antwort: C
Begründung:
The uploaded question set identifies self-driving, self-securing, and self-repairing as the defining Autonomous AI Database characteristics. Oracle documentation uses the same terminology when describing Autonomous Database services.
Self-driving refers to automated database-management activities that traditionally require significant DBA intervention, including provisioning, tuning, optimization, backups, patching, and scaling. Self-securing encompasses automated security practices designed to protect database infrastructure and data, including encryption and security maintenance. Self-repairing describes automated availability and fault-management capabilities intended to reduce downtime and recover from infrastructure or database failures with minimal manual involvement.
These attributes are central to Oracle's Autonomous Database strategy because the service shifts routine infrastructure and database operations from customer-managed procedures toward automated cloud-service capabilities. This allows development and data teams to focus on application logic and business workloads rather than routine database administration.
The alternative combinations are not Oracle's established characterization. Autonomous AI Database does not promise autonomous application coding, licensing, documentation creation, or architectural design. Those terms incorrectly broaden the scope of database automation beyond the capabilities Oracle associates with the Autonomous platform.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Autonomous characteristics and automated database operations.
40. Frage
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
Antwort: B
41. Frage
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