AI-901日本語 無料問題集「Microsoft Azure AI Fundamentals (AI-901日本語版)」
以下の各記述について、正しい場合は「はい」を選択してください。そうでない場合は「いいえ」を選択してください。
注：正解ごとに1ポイントが加算されます。
注：正解ごとに1ポイントが加算されます。
正解：
Explanation:
In Microsoft Foundry Agent Service, tool_choice controls whether and how an agent uses tools during a run.
Statement 1: Yes
Setting tool_choice to auto lets the agent/model decide whether to call a tool. This statement is true.
Statement 2: No
Setting tool_choice to none does not mean the model decides whether to call a tool. It means the model will not call a tool. This statement is false.
Statement 3: Yes
Setting tool_choice to required means the agent must call one or more tools during the run. This statement is true.
Microsoft documentation describes the tool_choice values as follows: auto allows the model to decide whether to call tools, none prevents tool calls, and required forces one or more tool calls.
文を正しく完成させる選択肢を選びなさい。
正解：
Explanation:
The Microsoft responsible AI principle of transparency requires that AI systems be explainable to users
.
Transparency means users should understand when they are interacting with AI, what the system can and cannot do, and how outputs or predictions are generated at an appropriate level.
The other options map to different responsible AI principles:
protect sensitive user data = Privacy and security
reduce bias in decisions = Fairness
require human oversight = Accountability
以下の各記述について、正しい場合は「はい」を選択してください。そうでない場合は「いいえ」を選択してください。注：正解ごとに1ポイントが加算されます。
正解：
Explanation:
Statement 1: Azure Content Understanding in Foundry Tools can analyze only PDF documents. = No Azure Content Understanding is not limited to PDF documents. It can analyze multiple content types, including documents, forms, images, audio, and video.
Statement 2: Azure Content Understanding in Foundry Tools results are returned in the JSON format.
= Yes
Azure Content Understanding returns structured analysis results in JSON format. This is how extracted fields, values, confidence scores, and other analysis results are represented.
Statement 3: Azure Content Understanding in Foundry Tools can extract structured fields from documents and forms. = Yes Azure Content Understanding can use analyzers and schemas to extract structured fields from documents and forms, such as invoices, receipts, contracts, and other business documents.
以下の各記述について、正しい場合は「はい」を選択してください。そうでない場合は「いいえ」を選択してください。
注：正解ごとに1ポイントが加算されます。
注：正解ごとに1ポイントが加算されます。
正解：
Explanation:
Statement 1: Generating a response to a user prompt occurs during the inference stage. = Yes When a deployed generative AI model receives a user prompt and generates an output, that process is called inference . The model is being used to produce a response, not being trained.
Statement 2: A generative AI model generates responses by copying stored documents directly from the model's training data. = No Generative AI models do not normally respond by simply copying stored documents from training data. They generate outputs based on learned statistical patterns. In some cases, models may reproduce memorized content, but that is not the normal or intended generation mechanism.
Statement 3: A generative AI model produces output by predicting the next token based on patterns learned from the model's training data. = Yes This is correct. Large language models generate text by predicting likely next tokens based on the prompt and learned patterns from training.
あなたは、Foundry Tools の Azure Content Understanding を使用して請求書を分析するアプリケーションを開発しています。
invoiceAnalyzer という名前のカスタムアナライザーがあります。
請求書ファイルを処理するには、アナライザーを使用する必要があります。
Pythonコードをどのように完成させるべきですか？回答するには、回答欄で適切なオプションを選択してください。
注：正解ごとに1ポイントが加算されます。
invoiceAnalyzer という名前のカスタムアナライザーがあります。
請求書ファイルを処理するには、アナライザーを使用する必要があります。
Pythonコードをどのように完成させるべきですか？回答するには、回答欄で適切なオプションを選択してください。
注：正解ごとに1ポイントが加算されます。
正解：
Explanation:
The correct parameter is analyzer_id . The Azure AI Content Understanding Python SDK uses the ContentUnderstandingClient.begin_analyze() method to submit content for processing. Microsoft defines the method signature with analyzer_id as the required identifier that specifies which analyzer should process the supplied input.
Therefore, the code should be completed as:
poller = client.begin_analyze(analyzer_id= " invoiceAnalyzer " , ...)
A custom analyzer encapsulates the configuration that determines how Content Understanding interprets the source content and which fields or structured information it extracts. Because the analyzer has already been created with the name invoiceAnalyzer , that value is supplied through analyzer_id when the analysis operation begins.
extraction_type is not the parameter used to select an analyzer. model_name would refer conceptually to an underlying AI model, but Content Understanding analysis is invoked through an analyzer abstraction rather than by specifying a model directly. Likewise, schema_id does not identify the analyzer to execute.
Microsoft ' s custom-analyzer guidance explicitly demonstrates client.begin_analyze (analyzer_id=analyzer_id, inputs=[...]) , confirming the required SDK pattern