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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Topic 2: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 3: Implement agentic solutions | 20-25% | - Build AI agents
|
| Topic 4: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 5: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
>> Valid AI-103 Test Objectives <<
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NEW QUESTION # 87
You plan to build an agent that will combine and process multiple files uploaded by users.
You are evaluating whether to use the Azure AI Agent Service to develop the agent.
What is the maximum size of each file that can be uploaded to the service?
Answer: D
Explanation:
https://learn.microsoft.com/en-us/azure/ai-services/agents/quotas-limits
NEW QUESTION # 88
You need to extract prebuilt entity categories including Person, Organization, Location, and DateTime from each email without training a model.
Answer: D
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
TextAnalyticsClient.recognize_entities invokes Azure Language ' s pretrained Named Entity Recognition (NER) capability. NER identifies recognized concepts in unstructured text and assigns supported categories such as Person, Organization, Location, DateTime, Quantity, and related entity classes.
No training dataset or custom model is required, which directly satisfies the question.
recognize_pii_entities is designed for personally identifiable and sensitive information. Although there is some overlap in certain entity classes, its purpose is privacy detection and redaction rather than general NER.
NamedEntityRecognitionSkill is an Azure AI Search enrichment skill. It would be appropriate if the text were being processed as part of an AI Search indexing pipeline, but the question states that the application already uses TextAnalyticsClient, so adding Search would unnecessarily increase complexity.
Custom Entity Recognition is required when an organization needs domain-specific entities not adequately covered by pretrained categories. It requires labeling training data and training a custom Language model, directly contradicting the requirement to avoid training.
The correct method is therefore A, recognize_entities .
Study Guide alignment: Implement text analysis solutions # extract entities, topics, summaries, and structured information using Foundry Tools .
NEW QUESTION # 89
You have a Microsoft Foundry project that contains an agent.
You use a GitHub Actions workflow for CI/CD.
You need to configure the workflow to automatically evaluate the agent when a pull request (PR) is created and prevent branches from merging if the evaluation results do NOT meet the defined thresholds.
How should you configure the workflow? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Authentication method: An Azure Login action that uses OpenID Connect (OIDC) If the evaluation results are NOT met, configure the workflow to: Fail The correct authentication method is Azure Login with OpenID Connect (OIDC) . Microsoft Foundry's GitHub Actions evaluation guidance recommends Microsoft Entra ID authentication and states that authentication can be automated by using the Azure Login GitHub action with OpenID Connect. The sample evaluation workflow also grants id-token: write, runs azure/login@v2, and then invokes the Microsoft AI Agent Evaluation action. This is the appropriate CI/CD authentication pattern because it avoids long-lived personal access tokens and supports secure federated authentication from GitHub Actions into Azure.
The workflow should be configured to fail when evaluation thresholds are not met. Foundry's evaluation GitHub Action is designed to automate pre-production assessment of Microsoft Foundry agents in CI/CD pipelines and produce evaluation results for the configured evaluators and test dataset. A failed GitHub Actions check can then be enforced through branch protection so the PR cannot merge until the quality gate passes. Locking the target branch or sending an alert does not directly implement a CI quality gate. Reference topics: Microsoft Foundry agent evaluation, GitHub Actions evaluation workflow, Microsoft Entra authentication, Azure Login with OIDC, pull-request quality gates, and CI/CD governance.
NEW QUESTION # 90
You need a connection method for a GPT realtime model that supports live client-side audio streaming and approximately 100-ms latency.
Answer: B
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use WebRTC . Microsoft ' s current Realtime API documentation explicitly recommends WebRTC for low- latency real-time audio streaming and lists it as the preferred connection type for client-side applications such as web and mobile apps. The documented latency is approximately 100 ms . Microsoft Learn WebRTC is designed specifically for interactive media and provides capabilities appropriate for continuous microphone input and audio playback, including low-latency media transport and handling of real-time network conditions.
Microsoft distinguishes this from WebSocket , which is positioned primarily for server-to-server/backend scenarios and has a higher typical latency of approximately 200 ms in the current guidance. SIP is appropriate when integrating telephony infrastructure such as call centers or IVR systems. RTMP is not the supported GPT Realtime interactive client protocol for this scenario. Microsoft Learn Therefore, the answer shown in the supplied file ( A, WebSocket ) conflicts with current Microsoft Learn documentation. For a browser/client application targeting approximately 100-ms audio latency, the verified answer is C, WebRTC .
Study Guide alignment: build multimodal/realtime generative applications and integrate speech as an agent modality . Microsoft Learn
NEW QUESTION # 91
You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?
Answer: C
Explanation:
The correct answer is A. an Azure Content Understanding in Foundry Tools analyzer . In Azure Content Understanding, an analyzer is the reusable configuration that defines what content type is processed, which elements are extracted, and how the output is structured. Microsoft's analyzer guidance states that analyzers define extraction for text, layout, tables, fields, and output formats such as Markdown and JSON. This makes the analyzer the first required configuration step before downstream reasoning or agent orchestration can use the extracted content.
This matches the requirement because the documents contain scanned text, tables, and multicolumn layouts.
Content Understanding document analysis is designed to transform unstructured documents into structured, machine-readable output while preserving document structures and relationships. Its Markdown representation converts unstructured documents into GitHub Flavored Markdown while maintaining content and layout for downstream use.
A generative chat completion request or Azure OpenAI Responses API call could reason over extracted content, but it is not the correct first step for OCR, layout preservation, and Markdown conversion. Azure Language focuses on text analysis after content has already been extracted. Reference topics: Content Understanding analyzers, document extraction, OCR, layout analysis, Markdown output, and downstream reasoning.
NEW QUESTION # 92
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