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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Building Low-Code Applications and Agentic AI | 10% | - Describe Oracle APEX as Oracle's low-code platform - Choose the appropriate Agent Factory capability for a no-code AI agent use case |
| Topic 3: 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 4: 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 5: Using Oracle Database Actions and Data Studio Tools | 15% | - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks - Describe Database Actions and core development tools |
| Topic 6: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Create an Autonomous AI Database Serverless instance for a basic workload - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Describe Autonomous AI Database characteristics, offerings, and deployment choices |
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NEW QUESTION # 12
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
Answer: D
NEW QUESTION # 13
Which set of characteristics is commonly used to describe Autonomous AI Database?
Answer: A
Explanation:
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.
NEW QUESTION # 14
How can developers create vectors for data objects in Oracle AI Database 26ai?
Answer: C
Explanation:
Oracle AI Database 26ai provides native SQL and PL/SQL facilities for generating vector embeddings from source data. A principal example is the VECTOR_EMBEDDING SQL function, which generates an embedding by applying an embedding or feature-extraction model to an input value. Oracle also provides vector utilities such as UTL_TO_EMBEDDING, DBMS_VECTOR, and DBMS_VECTOR_CHAIN for vectorization, chunking, embedding generation, similarity-search pipelines, and integration with supported embedding providers.
When an embedding model is imported into Oracle AI Database-for example, in supported ONNX form- the database can perform text-to-vector transformation internally. Oracle also supports accessing external embedding providers through REST where appropriate, but sending all data to a separate vector database or service is not a prerequisite. Keeping vectorization and vector storage within Oracle AI Database can reduce data movement and enables embeddings to remain integrated with the underlying business objects.
Vectors also do not need to be manually represented as JSON documents, and Property Graph Views serve a different purpose: modeling entities and relationships. Consequently, the built-in vectorization capability is the direct Oracle-native mechanism described by the question. The uploaded source explicitly identifies option A as correct.
Study Guide reference: Working with AI and Vector Foundations - vector generation, VECTOR_EMBEDDING, vector utilities, embedding models, and native AI Vector Search.
NEW QUESTION # 15
What is the main difference between Autonomous AI Database Serverless and Dedicated deployment choices?
Answer: A
Explanation:
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.
NEW QUESTION # 16
In a RAG-style application, where does similarity search apply?
Answer: C
Explanation:
Similarity search performs the retrieval component of Retrieval-Augmented Generation. Source documents or other content are converted into vector embeddings and stored in a vector-capable database. The user's question is similarly transformed into an embedding, and a vector-distance or similarity calculation identifies stored embeddings whose semantic meaning is closest to the query. Oracle describes semantic similarity search as identifying and retrieving data points that closely match a query by comparing feature vectors in the vector store.
The resulting documents or chunks provide relevant contextual information to the generative model. Select AI with RAG, for example, retrieves content from a configured vector store through semantic similarity search and places that content into an augmented prompt sent to the LLM. The LLM then generates a response grounded in retrieved enterprise information. Similarity search therefore does not create the original documents, perform authentication, or eliminate response generation. Returning raw vectors alone would also fail to achieve RAG's purpose because the retrieved source content must ultimately inform the generated response. The uploaded question set confirms the retrieval of relevant stored content as the correct function.
Study Guide reference: Working with AI and Vector Foundations - semantic similarity search, vector retrieval, embeddings, and RAG architecture.
NEW QUESTION # 17
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