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Both theories of knowledge as well as practice of the questions in the AI-901 practice quiz will help you become more skillful when dealing with the exam. Our experts have distilled the crucial points of the exam into our AI-901 Training Materials by integrating all useful content into them. And you will find that it is easy to understand the content of the AI-901 learning guide for our experts have simplified the questions and answers.
NEW QUESTION # 68
Your company processes customer support emails.
You need to implement an AI solution that automatically identifies mentions of people, organizations, and locations in the emails.
Which text analysis technique should you use?
Answer: B
Explanation:
The adequate text analysis feature is Named Entity Recognition (NER), which is a core prebuilt capability of the Azure AI Language service.
Prebuilt Categorization: The prebuilt NER feature automatically parses unstructured text (like emails) to identify and group elements into standard classes, including Person, Organization, and Location.
No Training Required: Because it relies on state-of-the-art pretrained transformer models, it can be deployed immediately out-of-the-box without requiring data labeling or custom model training.
High Extensibility: If your emails contain highly specialized terms (such as proprietary product IDs or industry-specific roles), you can easily scale to Custom NER within the same architecture.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/overview
NEW QUESTION # 69
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 70
Drag and Drop Question
You have a Microsoft Foundry project that contains a model deployment.
You are developing an application that sends an image and a user question to the model.
You need to send both text and image content in the same request so the model can return an answer.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 71
You are developing an application that uses the Azure Language SDK and a Microsoft Foundry resource.
You need to return a list of important topics from unstructured text.
Which Python method should you use?
Answer: C
Explanation:
To return a list of important topics from unstructured text using the Azure Language Python SDK within a Microsoft Foundry resource, you should use the extract_key_phrases method.
Depending on whether you are running synchronous or asynchronous operations for large batches of documents, choose one of the following methods from the TextAnalyticsClient:
client.extract_key_phrases(documents)
Used for immediate, synchronous processing of text data. It returns results in real time and is ideal for smaller or interactive application workloads.client.begin_extract_key_phrases(documents) Used for asynchronous (batch) processing. It returns a long-running operation poller, which is highly recommended when analyzing large volumes of unstructured text or multiple documents simultaneously.
Reference:
https://notes.kodekloud.com/docs/AI-102-Microsoft-Certified-Azure-AI-Engineer-Associate/Analyzing-Text/Working-with-Azure-AI-Language-Services/page
NEW QUESTION # 72
Select the answer that correctly completes the sentence.
Answer:
Explanation:
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.
NEW QUESTION # 73
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