有了VCESoft的1z0-1195-26考古題，即使你只用很短的時間來準備考試，你也可以順利通過考試。因為VCESoft的考古題包含了在實際考試中可能出現的所有問題，所以你只需要記住1z0-1195-26考古題裏面出現的問題和答案，你就可以輕鬆通過考試。這是通過考試最快的捷徑了。如果你工作很忙實在沒有時間準備考試，但是又想取得1z0-1195-26的認證資格，那麼，你絕對不能錯過VCESoft的1z0-1195-26考古題。因為這是你通過考試的最好的，也是唯一的方法。
| Section | Objectives |
|---|---|
| Building Low-Code Applications and Agentic AI | - Describe Oracle APEX as Oracle's low-code platform - Choose the appropriate Agent Factory capability for a no-code AI agent use case |
| Implementing Select AI and AI Vector Search 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 - Describe Select AI in Autonomous AI Database |
| Using Oracle Database Actions and Data Studio Tools | - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks - Describe Database Actions and core development tools |
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Create an Autonomous AI Database Serverless instance for a basic workload - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy |
| Working with JSON and Graph in Oracle AI Database | - Describe core graph concepts and graph analytic capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case - Explain JSON and Oracle AI Database JSON capabilities |
| Working with AI and Vector Foundations | - Explain vectors, embeddings, and the Oracle VECTOR data type - Describe AI, AGI, and machine learning foundations - Apply vector distance and indexing concepts to similarity search needs |
VCESoft的產品不僅幫助客戶100%通過第一次參加的Oracle 1z0-1195-26 認證考試，而且還可以為客戶提供一年的免費線上更新服務，第一時間將最新的資料推送給客戶，讓客戶瞭解到最新的考試資訊。所以VCESoft不僅是個產品品質很好的網站，還是個售後服務很好的網站。
問題 #39
A business team wants to launch a no-code AI agent quickly. They prefer to start from a ready-made option and later refine the publishing workflow. Which Private Agent Factory capability path fits this need?
答案：D
解題說明：
Oracle AI Database Private Agent Factory is explicitly designed as a no-code environment for rapidly building, testing, and deploying intelligent agents. Oracle documents that Agent Factory supports pre-built agents, custom-built agents, and end-to-end workflows and includes curated agentic templates intended to accelerate implementation. Starting from one of these ready-made assets minimizes initial design work and is therefore the strongest match for a business team that prioritizes rapid deployment.
If additional customization becomes necessary, Agent Builder provides a visual no-code environment for constructing and refining agents and workflows from modular components. Oracle describes capabilities including drag-and-drop workflow construction, data connectors, LLM integration, APIs, custom agent creation, multi-agent orchestration, and reusable templates. This establishes a logical progression: begin with a pre-built agent/template to obtain functionality quickly, then move into Agent Builder when deeper customization or workflow tailoring is required. Starting from a completely blank agent would unnecessarily increase implementation effort, while a prompt-only prototype bypasses Agent Factory's governed agent capabilities. The source question likewise identifies the pre-built-to-Agent-Builder path as correct.
Study Guide reference: Building Low-Code Applications and Agentic AI - Private Agent Factory, pre-built agents, templates, and Agent Builder.
問題 #40
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?
答案：C
解題說明：
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.
問題 #41
What is an embedding in the context of AI Vector Search?
答案：B
解題說明：
An embedding is a numerical vector representation of the meaning or characteristics of some source content.
Oracle describes vector embeddings as mathematical vector representations that capture semantic meaning for content such as words, documents, images, audio, or other data objects. The embedding model transforms the source input into an ordered sequence of numerical values, placing semantically related items close together within a multidimensional vector space.
This numerical representation enables AI Vector Search. Instead of comparing documents solely by literal keyword equality, Oracle can calculate mathematical distances between embeddings. Smaller distances generally identify objects that are semantically more closely related according to the embedding model. This supports capabilities such as semantic document retrieval, recommendation systems, image similarity, RAG, and natural-language search.
An embedding is therefore not a backup mechanism, database maintenance configuration, or graph label.
Those concepts belong to unrelated database subsystems. The crucial certification distinction is that the original content and its embedding are different representations: the content may be text, image, audio, or another object, while its embedding is a numeric vector representing learned semantic characteristics. The uploaded assessment identifies "a sequence of numbers that represents semantic content" as the correct description.
Study Guide reference: Working with AI and Vector Foundations - embeddings, semantic representations, vector spaces, and similarity search.
問題 #42
What does Select AI enable in Autonomous AI Database?
答案：A
解題說明：
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.
問題 #43
A company must control the lifecycle of its database encryption keys to satisfy regulatory requirements.
Which key management option should it use for the Oracle Autonomous AI Database instance?
答案：C
解題說明：
Customer-managed encryption keys integrated with OCI Vault are appropriate when an organization requires direct control over encryption-key lifecycle operations for security, governance, or regulatory compliance.
Autonomous AI Database uses Transparent Data Encryption to protect database data and supports both Oracle-managed and customer-managed master encryption keys. With the default Oracle-managed approach, Oracle performs key-management operations. With customer-managed keys, the organization creates and manages a master key in a supported external key-management system such as OCI Vault.
OCI Vault centralizes secure key storage and enables the customer to control operations such as key creation, rotation, lifecycle governance, access policy, and auditing. Autonomous AI Database then uses the customer- managed master encryption key as part of the TDE key hierarchy. This directly addresses the stated requirement for organizational control of encryption keys. Public certificates are intended for network identity and TLS-related functions rather than TDE key lifecycle management. APEX workspace configuration is unrelated to database master encryption keys. Oracle-managed keys provide strong encryption but do not satisfy a requirement specifically calling for customer-controlled lifecycle management.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Autonomous AI Database security, TDE, OCI Vault, and customer-managed encryption keys.
問題 #44
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1z0-1195-26考題: https://www.vcesoft.com/1z0-1195-26-pdf.html