1z0-1157-26試験資格証明書を取得することは難しいです。でも、Oracle 1z0-1157-26復習教材を選ばれば、試験に合格することは簡単です。1z0-1157-26復習教材の内容は全面的で、価格は合理的です。そして、Oracleはお客様にディスカウントコードを提供でき、1z0-1157-26復習教材をより安く購入できます。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
| Topic 2: OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Topic 3: Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
| Topic 4: Introduction to AI Agents | 15% | - AI agent fundamentals
|
| Topic 5: Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
| Topic 6: OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
成功への道を示す指標として、当社の1z0-1157-26実践教材は、あなたの旅のあらゆる困難を乗り越えるために役立ちます。 すべての課題をウォークインのように扱うことはできませんが、1z0-1157-26シミュレーションの実践により、レビューを効果的にすることができます。 それが、当社の1z0-1157-26調査問題がプロのモデルである理由です。 98％以上の高い合格率を誇る1z0-1157-26試験問題により、数千万人の受験者が試験に合格しました。
質問 # 38
In the OpenAI Agents SDK, what is the role of the Runner?
正解：A
解説：
The Runner is responsible for executing the OpenAI Agents SDK agent loop. The uploaded course source identifies this directly as the Runner's role.
When Runner.run() , Runner.run_sync() , or Runner.run_streamed() is invoked, the Runner starts with an agent and user input, calls the configured model, evaluates the model output, and decides what happens next.
If the output is final, execution terminates. If the model requests a tool call, the Runner executes the tool, appends the result, and calls the model again. If the model produces a handoff, the Runner updates the active agent and continues the loop. OpenAI's official documentation describes precisely this lifecycle.
The Runner is therefore an orchestration/runtime component rather than an agent-hosting deployment service.
Authentication configuration exists separately, and function-tool JSON schemas are generated by the tool- definition mechanisms rather than being the Runner's primary responsibility.
This distinction is central to the SDK architecture: the Agent defines behavior and capabilities , while the Runner executes the iterative workflow .
Therefore, C is correct.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - Runner, agent loop, tool execution, handoffs, final output, and runtime orchestration.
質問 # 39
What is the default distance metric for VECTOR_DISTANCE in Oracle for non-BINARY vectors?
正解：C
解説：
Oracle AI Vector Search defines VECTOR_DISTANCE as the primary SQL function for calculating the distance between two vectors. When the function is called without explicitly specifying a distance metric, Oracle specifies COSINE as the default metric for ordinary, non-BINARY vectors. Cosine distance measures the angular relationship between vector representations and is widely used for semantic similarity because embeddings with similar meaning tend to point in similar directions in vector space. Oracle treats BINARY vectors differently: their default metric is HAMMING. Euclidean, or L2, distance is supported but must be selected when required; it is not the general default. Levenshtein distance applies to string-edit comparisons, while bitwise XOR is not the default Oracle vector-distance metric. Therefore, for the scenario stated in the question, option C is the verified answer. Oracle Docs
質問 # 40
In the given LangChain chain, what is the role of StrOutputParser()? chain = prompt
正解：B
解説：
StrOutputParser is a LangChain output parser used to convert a language-model response into a standard Python string. In an LCEL pipeline such as prompt | model | StrOutputParser() , the prompt prepares the model input, the model generates an AIMessage or equivalent model output, and StrOutputParser extracts the textual content so downstream application code receives plain text.
LangChain's official documentation demonstrates this exact pattern by composing a prompt, chat model, and StrOutputParser() into a chain and then invoking the resulting runnable. The parser therefore operates after model inference ; it does not send the request to the model and does not maintain agent or tool history.
The uploaded source presents the chain fragment across separate lines and explicitly identifies "It extracts plain text from the model response" as the correct response.
This output-parsing stage is especially useful because it isolates application code from provider-specific response-object structures and provides a predictable string output from a LangChain runnable.
Study Guide reference/topic: LangChain for AI Agents - LCEL chains, Runnable composition, model output handling, and StrOutputParser.
質問 # 41
Which set lists built-in tool categories supported by OCI Enterprise AI Agents?
正解：A
解説：
OCI Enterprise AI Agents supports a defined set of OpenAI-compatible agent tools through the OCI Responses API. Oracle's current documentation identifies File Search, Code Interpreter, Function Calling, and MCP Calling as supported tool categories.
File Search allows an agent to retrieve relevant information from indexed content and vector stores. Code Interpreter provides a controlled environment for computational or programmatic analysis. Function Calling lets the model request execution of application-defined functions with structured parameters. MCP Calling enables the agent to discover and invoke capabilities made available by remote Model Context Protocol servers. Together, these mechanisms allow an LLM to move beyond text generation and perform retrieval, computation, application actions, and standardized external-system integration.
Oracle additionally provides supporting agent resources such as Files, Vector Stores, Containers, Conversations, Projects, and memory capabilities, while SQL Search/NL2SQL is available as an OCI-native agent capability.
SSH, FTP, RDP, VCN routing, load balancing, SMS, and fax are not the four built-in tool categories identified in the OCI Enterprise AI Agents curriculum. Consequently, A is correct and agrees with the uploaded question set.
Study Guide reference/topic: OCI Enterprise AI Agents - File Search, Code Interpreter, Function Calling, MCP Calling, Vector Stores, and agent tools.
質問 # 42
What is long-term memory in OCI Enterprise AI Agents?
正解：C
解説：
OCI Enterprise AI Agents uses long-term memory to preserve useful information beyond the lifetime of an individual conversation. Oracle documents long-term memory as durable memory across conversations , associated through a subject identifier within an OCI Generative AI project. When enabled, important information can be extracted from conversations, converted into embeddings, persisted, and retrieved during subsequent interactions involving the same subject. This differs from short-term memory, which primarily maintains or compacts context within an ongoing conversation. Long-term memory is therefore not the model's pretraining corpus, a fixed training dataset, or general-purpose container block storage. It is an agent- oriented context mechanism designed to improve continuity and personalization while keeping memory governed within project boundaries. Consequently, option C precisely reflects Oracle's documented Enterprise AI Agents architecture. Oracle Docs
質問 # 43
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Oracleの1z0-1157-26認定試験を受験したいですか。試験がたいへん難しいですから悩んでいるのですか。試験を申し込みたいですが、合格できないことが心配します。いまこのような気持ちを持っていますか。大丈夫ですよ。安心に1z0-1157-26試験を申し込みましょう。Jpexamの試験参考書を使用する限り、どんなに難しい試験でも問題にならないです。試験に合格する自信を全然持っていなくても、Jpexamの1z0-1157-26問題集はあなたが一度簡単に成功することを保証できます。不思議と思っていますか。では、Jpexamのウェブサイトへ来てもっと多くの情報をブラウズすることもできます。それに、1z0-1157-26問題集の一部を試用することもできます。そうすると、この参考書が確かにあなたが楽に試験に合格する保障ということをきっと知るようになります。
1z0-1157-26シュミレーション問題集: https://www.jpexam.com/1z0-1157-26_exam.html