當你進入KaoGuTi網站，你看到每天進入KaoGuTi網站的人那麼多，不禁感到意外。其實這很正常的，我們KaoGuTi網站每天給不同的考生提供培訓資料數不勝數，他們都是利用了我們的培訓資料才順利通過考試的，說明我們的Oracle的1z0-1195-26考試認證培訓資料真起到了作用，如果你也想購買，那就不要錯過我們KaoGuTi網站，你一定會非常滿意的。
| Section | Objectives |
|---|---|
| Building Low-Code Applications and Agentic AI | - Choose the appropriate Agent Factory capability for a no-code AI agent use case - Describe Oracle APEX as Oracle's low-code platform |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | - Describe Select AI in Autonomous AI Database - Apply AI Vector Search to combined semantic and business-data search scenarios - Determine how AI Vector Search supports GenAI pipelines and RAG |
| Working with AI and Vector Foundations | - 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 |
| Working with JSON and Graph in Oracle AI Database | - 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 |
| 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 |
| Using Oracle Database Actions and Data Studio Tools | - Describe Database Actions and core development tools - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks |
我們會在互聯網上免費提供部分關於Oracle 1z0-1195-26 認證考試的練習題讓嘗試，您會發現KaoGuTi的練習題是最全面的，是你最想要的。
問題 #11
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?
答案：C
解題說明：
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.
問題 #12
What is OSON in Oracle AI Database JSON support?
答案：A
解題說明：
OSON is Oracle's optimized binary representation for JSON data. Oracle AI Database uses OSON as the native storage representation of the SQL JSON data type. Unlike textual JSON stored in VARCHAR2, CLOB, or BLOB values, native JSON data does not need to be repeatedly parsed from character representation for common processing operations. Oracle states that OSON is optimized for fast query and update operations in both the Oracle AI Database server and supported database clients.
The practical advantage is that applications retain JSON's flexible document model while gaining database- native processing efficiency, SQL integration, indexing capabilities, and transactional control. OSON therefore concerns the physical/optimized representation of JSON data, not the operational scheduling of JSON collections or the visualization of graph structures. It is also unrelated to the SQL Worksheet, which is a Database Actions development interface for executing SQL and PL/SQL. The uploaded question explicitly identifies "An optimized binary format for JSON storage" as the correct answer.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - native JSON data type, OSON binary JSON representation, JSON query and update processing.
問題 #13
What is the main difference between Autonomous AI Database Serverless and Dedicated deployment choices?
答案：D
解題說明：
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.
問題 #14
Which example is a common AI use case?
答案：B
解題說明：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Fraud detection is a standard AI use case because machine-learning and anomaly-detection models can identify unusual behavioral patterns across transaction attributes such as amount, account, merchant, location, and historical activity. Oracle documents AI-driven fraud-prevention architectures that analyze transaction behavior and flag suspicious or anomalous activity for investigation. The other options are administrative database or network tasks rather than AI inference problems. Alphabetically listing tables is ordinary metadata browsing; rotating an encryption key is deterministic security administration; and assigning a subnet CIDR is infrastructure configuration. None requires a model to learn patterns from data. Detecting fraudulent transactions from behavior patterns therefore best represents an AI workload and aligns with the Oracle AI Database foundations coverage of practical AI domains and use cases. Oracle Docs
問題 #15
What does Select AI enable in Autonomous AI Database?
答案：C
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 #16
......
最近，身邊考 Oracle 認證的人也是相當多的，那麼，怎麼去準備 1z0-1195-26 考試呢？建議大家，可以先到考試中心去打聽這科考試的有關的情況。了解考試的流程，考試的注意事項。預約一個合適的時間去報名參加考試即可。為了更有把握的通過考試，可以看看KaoGuTi 考題網的 1z0-1195-26 題庫，上面的題目都是真題，很准，我做了很多遍的練習。練習題有些部分超出了 Oracle 的要求，但是對於扎實的掌握知識是很有幫助的，建議做完，搞懂。這是你輕鬆通過考試的最好的方法。
1z0-1195-26證照: https://www.kaoguti.com/1z0-1195-26_exam-pdf.html