Microsoft Azure AI Fundamentals (AI-901日本語版) - AI-901日本語 FREE EXAM DUMPS QUESTIONS & ANSWERS

Explanation:
Azure Text Analytics client library
The completed sentence is:
To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.
Azure Language text analysis features include sentiment analysis, key phrase extraction, named entity recognition, language detection, and other text analytics capabilities. These are accessed by using the Azure Text Analytics client library .
The highlighted Azure Speech SDK option is incorrect because it is used for speech workloads, such as speech recognition and speech synthesis, not text analysis.

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Explanation:
* Fairness
* Privacy and security
* Transparency
* Reliability and safety
Comprehensive and Detailed Explanation with all Azure AI documents : =
Evaluating model outputs to ensure that decisions are NOT biased against specific demographic groups:
Fairness
Microsoft's responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias across demographic groups.
Encrypting sensitive customer data and restricting system access to authorized personnel: Privacy and security The privacy and security principle requires AI systems to protect personal and sensitive data and prevent unauthorized access.
Informing users when they are interacting with an AI system and explaining the system's capabilities and limitations: Transparency The transparency principle requires that people understand when they are interacting with AI and understand the system's purpose, capabilities, and limitations.
Testing AI systems under different conditions to reduce unexpected failures: Reliability and safety The reliability and safety principle means AI systems should operate reliably and safely, including through testing and validation under expected and unexpected conditions.

Explanation:
The correct selection is AIProjectClient . In the Microsoft Foundry SDK, AIProjectClient serves as the primary project-level client for accessing and managing resources associated with a Foundry project.
Microsoft documentation specifically exposes operation groups through this client for deployments , agents , and indexes , which directly matches the requirements stated in the question.
For example, the SDK exposes deployments operations for working with models deployed to the project, agents operations for creating and managing agents, and indexes operations for accessing project search indexes. Microsoft also describes the Azure AI Projects client library as a unified Foundry SDK component that connects applications to project resources through a single project endpoint.
ChatCompletionsClient is focused primarily on model inference and chat-completion operations rather than comprehensive project-resource management. FoundryLocalManager is associated with local Foundry capabilities, while ModelCatalogClient would relate to model discovery/catalog functionality rather than deployments, agents, and indexes collectively.
Therefore, the class required for this scenario is AIProjectClient .

Explanation:

The completed code is:
poller = client.begin_analyze(
analyzer_id= " invoice " ,
input_url=url
)
result = poller.result()
Azure Content Understanding analysis uses a long-running operation pattern. The Python SDK returns a poller from begin_analyze(), and Microsoft documentation states that the SDK poller handles polling automatically when you call .result() .
Therefore, to retrieve the analysis results after processing completes, the correct option is:
result
The other options are incorrect because status checks operation state, wait waits without returning the final analysis object, and get_results is not the method shown for retrieving the begin_analyze() result in this code pattern.

Explanation:

Statement 1: To process prompts that contain images, you must deploy a multimodal generative AI model. = Yes Image prompts require a vision-enabled or multimodal model. A text-only model cannot interpret image content.
Statement 2: In the Foundry playground, you can provide an image by uploading a local file or by specifying a publicly accessible URL. = Yes The Foundry playground for vision-capable models supports adding image input to a prompt, including local uploads and image URLs.
Statement 3: A multimodal model can interpret multiple images included in a single prompt. = Yes Vision-enabled multimodal models can accept image content items in a prompt, and multiple images can be included for the model to analyze together.

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