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| Section | Objectives |
|---|---|
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Azure AI resource provisioning and configuration - Model selection and lifecycle management |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
>> Latest AI-103 Exam Topics <<
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NEW QUESTION # 106
You have an Azure subscription that contains an Azure App Service app named App1.
You provision a Microsoft Foundry Service resource named CSAccount1.
You need to configure App1 to access CSAccount1. The solution must minimize administrative effort.
What should you use to configure App1?
Answer: D
Explanation:
To configure the App Service app to access the Microsoft Foundry Service (part of Azure AI Services) with low administration, you need to configure the app with the endpoint URI and a subscription key.
These credentials can be found in the Azure Portal under the Keys & Endpoint section of your provisioned Foundry resource, allowing the app to seamlessly authenticate API calls.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/authentication
NEW QUESTION # 107
You have a Microsoft Foundry project that contains an internal Q & A agent.
Users report the following issues when they ask the agent questions:
* An increase in the following response: "No relevant information found"
* Periodic HTTP 429 rate limit exceeded errors during peak hours
You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Metrics to enable: Model Availability Rate and Provisioned Utilization
Diagnostic log to collect: trace
The correct metrics are Model Availability Rate and Provisioned Utilization . Model Availability Rate identifies whether failures are caused by service-side model unavailability because it is calculated from total calls minus server errors. Provisioned Utilization identifies resource-limit conditions because Microsoft states that when utilization reaches or exceeds 100%, calls are throttled and HTTP 429 errors are returned. This directly maps to the reported peak-hour rate-limit errors.
The correct diagnostic log is trace . For an internal Q & A scenario, trace logging is required to analyze question-answering behavior, including unanswered questions. Microsoft's custom question answering analytics guidance states that diagnostic logging stores telemetry and chat logs and instructs enabling Trace in addition to Audit, RequestResponse, and AllMetrics. The sample Kusto queries for unanswered questions inspect the answer, question, score, and knowledge base ID, and filter unanswered results by a score of zero.
RequestResponse is useful for request status and latency, and Audit is for administrative operations, but neither is the best diagnostic category for analyzing Q & A inference behavior such as "No relevant information found." Reference topics: Foundry monitoring, model availability, provisioned utilization, diagnostic logging, custom question answering analytics, and trace logs.
NEW QUESTION # 108
Hotspot Question
You have a Microsoft Foundry project that contains a customer support agent built by using the Foundry Agent Service.
The agent uploads user-provided screenshots to Azure Storage through a ticketing tool and receives a blob URL for additional reasoning.
You need to use image moderation during agent runs and prevent harmful content from being returned during runs. Azure AI Content Safety must access the images by using the blob URL.
The solution must follow the principle of least privilege.
What should you configure for Content Safety? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 109
Hotspot Question
You have a Microsoft Foundry project that contains an agent named PaymentAgent.
PaymentAgent includes a function tool that issues customer refunds by using an external API.
You are creating a workflow in YAML.
You need to ensure that the workflow pauses for human approval and continues with the refund step only after approval is granted.
How should you complete the workflow definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 110
You need to recommend an invoice review solution that resolves the issue reported by the finance department.
What should you include in the recommendation?
Answer: B
Explanation:
The correct recommendation is Azure Content Understanding in Foundry Tools . The case study states that Contoso's finance department must manually review vendor invoices to verify that invoice details match vendor contract terms, and that the invoices contain tables, logos, and varied layouts that make consistent processing difficult. It also states that the planned solution must evaluate both the visual layout and textual content of the invoices.
Azure Content Understanding is designed for this type of multimodal document-processing workload.
Microsoft describes Content Understanding as a Foundry Tool that processes unstructured and multimodal content, including documents and images, and transforms it into structured output for AI applications. It can use document analyzers to extract text, layout, tables, fields, and relationships from diverse document types.
Chat completions alone would not reliably extract structured invoice fields from complex layouts. Azure Document Intelligence can extract OCR, layout, and tables, but Content Understanding is the better end-to- end Foundry capability for combining visual and textual understanding with structured extraction for downstream verification. Image Analysis focuses on image-level visual features and is insufficient for invoice field and table review. Reference topics: Content Understanding, document analyzers, multimodal extraction, invoice processing, tables, layout, and structured JSON output.
NEW QUESTION # 111
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