Microsoft AI-103 Fragen und Antworten, Developing AI Apps and Agents on Azure Prüfungsfragen

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Microsoft AI-103 Exam Syllabus Topics:

SectionObjectives
Topic 1: Implement Computer Vision Solutions- Image classification and object detection
- OCR and document intelligence
Topic 2: Plan and Manage Azure AI Solutions- Model selection and lifecycle management
- Azure AI resource provisioning and configuration
- Responsible AI principles and governance
Topic 3: Develop Generative AI Applications and Agents- AI agents architecture
  • 1. Agent orchestration and workflows
    • 2. Memory and state management
      - Azure OpenAI Service integration
      • 1. Function calling and tool use
        • 2. Prompt engineering and prompt flow design
          Topic 4: Knowledge Mining and Information Retrieval- Indexing and semantic search
          - Azure AI Search configuration
          - RAG (Retrieval Augmented Generation) patterns
          Topic 5: Implement Natural Language Processing Solutions- Language understanding and intent recognition
          - Translation and multilingual support
          - Text analytics and summarization

          >> AI-103 Fragenkatalog <<

          AI-103 Deutsche Prüfungsfragen & AI-103 Prüfungs

          Jetzt ist die Microsoft AI-103 Zertifizierungsprüfung die beliebteste Zertifizierungsprüfung, an der viele IT-Fachleute beteiligen wollen. Dies ist ein Beweis für die IT-Fähigkeiten. Um die Prüfung zu bestehen sind umfangreiche Fachkenntnisse und Erfahrungen erfordert. Und das braucht doch viel Zeit. Vielleicht wählen Sie Ausbildungskurse oder Prüfungsmaterialien. Es ist eher kostengünstig, ein Ausbildungsinstitut von guter Qualität zu wählen. EchteFrage ist eine Website, die die Bedürfnisse der IT-Fachleute zur Microsoft AI-103 Zertifizierungsprüfung abdecken können. Die Produkte von EchteFrage sind ziegerichtete Ausbildung zur Microsoft AI-103 Zertifizierungsprüfung. Sie können in kurzer Zeit ihre IT-Fachkenntnisse ergänzen und sich gut auf die Microsoft AI-103 Zertifizierungsprüfung vorbereiten.

          Microsoft Developing AI Apps and Agents on Azure AI-103 Prüfungsfragen mit Lösungen (Q60-Q65):

          60. Frage
          You are deploying a support agent that enables users to upload photos.
          You need to automatically classify uploaded images for harmful content. The solution must block content based on severity levels.
          What should you do?

          Antwort: A

          Begründung:
          The correct answer is A. Implement image moderation . Azure AI Content Safety provides image analysis that classifies uploaded images for harmful content, including harm categories such as hate, sexual content, violence, and self-harm. Microsoft's Content Safety overview states that the Analyze Image API scans images for harmful content with multi-severity levels, which directly matches the requirement to automatically classify uploaded photos and block content based on configured severity thresholds.
          Prompt Shields are intended to detect prompt injection and jailbreak-style attacks against generative models, not to classify image harm categories. Keyword scanning OCR output would only detect visible text extracted from the image and would miss visual harm in the image itself. Blocklists can help match known words or custom patterns, but they are not a complete image safety classifier and do not provide the built-in severity- based image harm classification required here. Image moderation is therefore the correct control for user- uploaded photos. Reference topics: Azure AI Content Safety, image moderation, harm categories, severity levels, Foundry guardrails, and responsible AI controls.


          61. Frage
          You are building an app by using the Semantic Kernel.
          You need to include complex objects in the prompt templates of the app. The solution must support objects that contain subproperties.
          Which two prompt templates can you use? Each correct answer presents a complete solution.
          NOTE: Each correct selection is worth one point.

          Antwort: C,D

          Begründung:
          Semantic Kernel provides support for the following template formats:
          semantic-kernel - Built-in Semantic Kernel format.
          handlebars - Handlebars template format.
          liquid - Liquid template format
          The Semantic Kernel prompt template language is a simple way to define and compose AI functions using plain text. You can use it to create natural language prompts, generate responses, extract information, invoke other prompts or perform any other task that can be expressed with text.
          Reference:
          https://learn.microsoft.com/en-us/semantic-kernel/concepts/prompts/prompt-template-syntax


          62. Frage
          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.

          Antwort:

          Begründung:

          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.


          63. Frage
          You have a custom agent named Agent1.
          You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
          What should you do first?

          Antwort: B

          Begründung:
          To monitor and control access to a custom agent in Azure, you must first create a Microsoft Foundry project. Once the project is created, you register your custom agent within it to enable management capabilities such as access control and activity monitoring.
          Reference:
          https://learn.microsoft.com/en-us/azure/foundry/control-plane/register-custom-agent


          64. Frage
          Drag and Drop Question
          You have a Microsoft Foundry project that contains an agent. The agent uses threads and file uploads and calls an Azure OpenAI model deployment.
          During load testing, calls intermittently fall and return an HTTP 429 rate limit exceeded error.
          Some user uploads fail and generate an HTTP 400 file size exceeded error.
          You need to mitigate the errors and reduce call failures. The solution must remain within the service and model limits.
          What should you do to resolve each error? To answer, drag the appropriate actions to the correct errors. Each action 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 comet selection is worth one point.

          Antwort:

          Begründung:

          Explanation:
          Box 1: Implement exponential backoff and jitter in the retry logic
          To remedy intermittent HTTP 429 rate limit errors while staying within service limits, you must implement a retry policy that uses exponential backoff and jitter.
          Because the agent uses threads and file uploads, load testing triggers short-window concurrency spikes (bursting). Azure OpenAI evaluates rate limits in small slices (like 1- to 10-second windows), meaning overlapping file processing or concurrent thread steps will trigger a 429 even if your total Tokens-Per-Minute (TPM) or Requests-Per-Minute (RPM) look safe overall.
          Box 2: Split content into smaller files before uploading the files.
          Instead of uploading a large document in a single standard files.create request, utilize the Chunked Uploads REST API path. This splits the file payload on your client application layer before streaming it to Azure.
          Note: To resolve the HTTP 400 file size exceeded error in your Microsoft Foundry agent, you need to bypass the strict payload constraints of standard single-request uploads. In Azure OpenAI and Microsoft Foundry Agent architectures, a multipart standard upload has a rigid request body limit (typically 30 MB or 50 MB.
          Reference:
          https://learn.microsoft.com/en-us/answers/questions/2265002/getting-error-after-deployed-a-model-in-azure-ai-f
          https://learn.microsoft.com/en-us/answers/questions/5521436/getting-400-error(request-body-too-large)-for-batc


          65. Frage
          ......

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