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| Section | Objectives |
|---|---|
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Implement Natural Language Processing Solutions | - Translation and multilingual support - Language understanding and intent recognition - Text analytics and summarization |
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NEW QUESTION # 94
You have an Azure subscription that contains an Azure OpenAI resource.
You plan to build an agent by using the Azure AI Agent Service. The agent will perform the following actions:
- Interpret written and spoken questions from users.
- Generate answers to the questions.
- Output the answers as speech.
You need to create the project for the agent.
What should you use?
Answer: C
Explanation:
Azure AI Foundry is a platform for designing, customizing, managing, and supporting AI applications and agents. It acts as an AI app factory, providing a unified environment with tools, models, and deployment pipelines for various AI tasks. It enables teams to build and operate AI solutions, including those powered by generative AI, while ensuring security, governance, and cost-efficiency.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-azure-ai-foundry
NEW QUESTION # 95
You have a factory that produces cardboard packaging for food products. The factory has intermittent internet connectivity.
The packages are required to include four samples of each product.
You need to build an Azure Custom Vision model that will identify defects in packaging and provide the location of the defects to an operator. The model must ensure that each package contains the four products.
Which project type and domain should you use? To answer, move the appropriate options to the correct targets. You may use each option once, more than once, or not at all. you may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The model must use object detection because the solution needs more than an overall classification. It must identify individual defects, provide their locations, and count individual product samples. Object-detection predictions contain bounding-box coordinates in addition to labels and confidence scores, allowing the application to determine where a defect occurred and whether exactly four product instances are present.
The appropriate domain is General (compact) . The packaging and products are organization-specific and do not map to a more specialized object-detection domain. More importantly, a compact domain supports model export for edge/local inference . This matters because the factory has intermittent internet connectivity; the exported model can execute locally rather than depending continuously on a hosted Azure prediction endpoint.
Microsoft documents compact domains as optimized for deployment outside the Custom Vision hosted service. Exportable formats can include ONNX, TensorFlow, TensorFlow Lite, and other target-specific formats depending on project type and domain.
An ordinary General-domain model is intended for Azure-hosted prediction and cannot satisfy the same offline export requirement. Image classification is also unsuitable because it cannot return defect locations or count independently detected products.
Study Guide alignment: identify objects/components/regions and deploy vision functionality appropriate for edge scenarios . Microsoft Learn
NEW QUESTION # 96
You are defining an agent in Microsoft Foundry Agent Service. The agent uses a catalogue model for reasoning, a system instruction that sets its goals, and a file search capability that reads a knowledge store. Which three components does this combination represent?
Answer: C
Explanation:
Microsoft Foundry Agent Service defines every agent as the combination of three core components: a model that provides reasoning, instructions that set goals and behaviour, and tools that give access to data or actions. The scenario maps exactly, with the catalogue model, the system instruction, and the file search tool.
NEW QUESTION # 97
You have a Semantic Kernel agent that provides answers to product questions.
You plan to extend the capabilities of the agent to answer questions about the status of orders.
You need to ensure that the agent answers order status questions as quickly as possible. The solution must minimize development effort.
What should you include in the solution?
Answer: C
Explanation:
To enable the agent to answer questions about order status, you need a plugin.
In Semantic Kernel, a plugin acts as a container for the specific capabilities (functions) that allow an agent to interact with external data or systems, such as an order database or API.
To help me give you a more specific example or guide, could you tell me:
The data source where order info is kept (e.g., a SQL database, a REST API, or Shopify) The programming language you're using for the Kernel (C# or Python) Reference:
https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-functions
NEW QUESTION # 98
You have an application that processes scanned PDF invoices. The invoices have varied layouts and include multipage tables.
You have a pipeline that uses optical character recognition (OCR) and extracts totals and invoice numbers.
The results are often
incorrect because the document structure is ignored.
You need to implement a solution that provides OCR, layout analysis, and template-generalizing field extraction. The solution must NOT require training a custom model. The solution must minimize administrative effort.
What should you include in the solution?
Answer: B
Explanation:
The correct answer is Azure Content Understanding in Foundry Tools . The scenario requires more than basic OCR because scanned invoices have varied layouts and multipage tables. Content Understanding is designed for intelligent document processing and provides OCR, layout detection, table extraction, field extraction, confidence scores, and grounding in a managed service. Microsoft describes Content Understanding as a service that transforms unstructured content into structured outputs and supports invoice processing by extracting and validating fields from complex documents.
This also meets the requirement to avoid training a custom model. Content Understanding includes prebuilt and domain-specific analyzers, including invoice and procurement-style document processing, and Microsoft states that these analyzers provide structured extraction without custom training. It generalizes across visual template variations by using semantic document categories rather than requiring separate models per invoice layout.
Azure Machine Learning would increase administrative effort because it requires model development, training, deployment, and monitoring. Azure Language is optimized for text analytics tasks such as classification and entity extraction after text is available, but it does not provide document layout analysis or multipage table structure extraction. Reference topics: Content Understanding, intelligent document processing, OCR, layout analysis, analyzers, field schemas, and structured extraction.
NEW QUESTION # 99
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