1z0-1195-26試験問題の更新を1年以内にクライアントに無料で提供し、1年後にクライアントは50％の割引を受けることができます。クライアントが古いクライアントの場合、一定の割引を享受できます。当社Oracleの専門家は、毎日1z0-1195-26ガイドトレントを更新し、1z0-1195-26スタディガイドの最新の更新をクライアントに提供します。私たちはクライアントに割引を提供し、彼らがより少ないお金を使うようにします。あなたが古いクライアントである場合、あなたは特別割引を享受することができますので、お金を節約することができます。したがって、1z0-1195-26テストトレントを購入することは非常に価値があります。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Oracle Database Actions and Data Studio Tools | 15% | - Describe Database Actions and core development tools - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks |
| Topic 2: Working with JSON and Graph in Oracle AI Database | 20% | - Distinguish when graph capabilities and Property Graph Views fit a business use case - Explain JSON and Oracle AI Database JSON capabilities - Describe core graph concepts and graph analytic capabilities |
| Topic 3: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Create an Autonomous AI Database Serverless instance for a basic workload |
| 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: 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 6: Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - 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 |
もう既にOracleの1z0-1195-26認定試験を申し込みましたか。「もうすぐ試験の時間なのに、まだ試験に合格する自信を持っていないですが、どうしたらいいでしょうか。何か試験に合格するショートカットがあるのですか。試験参考書を読み終わる時間も足りないですから・・・」いまこのような気持ちがありますか。そうしても焦らないでくださいよ。試験を目前に控えても、ちゃんと試験に準備するチャンスもあります。何のチャンスですかと聞きたいでしょう。それはPassTestの1z0-1195-26問題集です。これは効果的な資料で、あなたを短時間で試験に十分に準備させることができます。この問題集の的中率がとても高いですから、問題集に出るすべての問題と回答を覚える限り、1z0-1195-26認定試験に合格することができます。
質問 # 22
What is the main difference between Autonomous AI Database Serverless and Dedicated deployment choices?
正解：B
解説：
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.
質問 # 23
In a RAG-style application, where does similarity search apply?
正解：A
解説：
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.
質問 # 24
Which set of characteristics is commonly used to describe Autonomous AI Database?
正解：B
解説：
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.
質問 # 25
Which sequence matches a simple RAG pipeline?
正解：C
解説：
A Retrieval-Augmented Generation pipeline depends on retrieval occurring before final response generation.
Source content is first processed into meaningful chunks. An embedding model converts those chunks into numerical vectors representing semantic meaning, and those vectors are stored in a vector store or indexed vector column. When a user submits a question, the question is also represented as an embedding. Similarity search then compares the query vector with stored vectors and retrieves the most semantically relevant chunks. Those retrieved chunks provide grounding context that is supplied to the LLM before it generates the final response.
Oracle AI Database supports this architecture through native vector storage, embedding generation, vector indexes, similarity functions, and Select AI RAG. Oracle specifically describes RAG as retrieving enterprise information through AI Vector Search and augmenting the prompt supplied to the LLM. Generating the response before retrieval defeats the fundamental purpose of RAG because the model would not yet have the grounding context. Likewise, graph modeling and workspace provisioning are not mandatory steps in the basic RAG pipeline. The question source identifies the embedding # storage # retrieval # generation sequence as correct.
Study Guide reference: Working with AI and Vector Foundations - embeddings, vector stores, semantic retrieval, and Retrieval-Augmented Generation.
質問 # 26
Which output can Select AI deliver to an application?
正解：D
解説：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Select AI supports multiple response modes depending on the action requested. With the default runsql action, Oracle generates SQL from the natural-language prompt, executes it, and returns the resulting data. The showsql action returns the generated SQL statement without executing it, while narrate executes the generated query and sends its results to the configured LLM to produce a natural-language description. Oracle additionally supports actions such as explainsql, chat, and summarize. Therefore, an application can receive a database result set, generated SQL, or a narrative response depending on how Select AI is invoked. Graph visualizations, patch-history maintenance recommendations, and automatic JSON export files are not Select AI output modes. This directly aligns with Oracle's Select AI actions and natural-language database interaction capabilities.
質問 # 27
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