AI-103 Schulungsangebot - AI-103 Musterprüfungsfragen

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Microsoft AI-103 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement information extraction and knowledge mining10–15%- Build knowledge bases and search solutions
  • 1. Design knowledge mining pipelines
  • 2. Implement Azure AI Search
  • 3. Create and manage vector indexes
- Extract structured data from documents
  • 1. Process forms, invoices, and unstructured content
  • 2. Use Azure AI Document Intelligence
Topic 2: Implement computer vision solutions10–15%- Build multimodal solutions
  • 1. Process and analyze video content
  • 2. Combine vision and language capabilities
- Implement image analysis and processing
  • 1. Implement object detection and image classification
  • 2. Use Azure AI Vision services
  • 3. Extract text and structure from images
Topic 3: Implement generative AI and agentic solutions30–35%- Build generative AI applications
  • 1. Build retrieval-augmented generation (RAG) solutions
  • 2. Implement function calling and tool use
  • 3. Integrate Azure OpenAI and other models
  • 4. Implement prompt engineering and optimization
- Design and implement intelligent agents
  • 1. Integrate agents with external systems and data sources
  • 2. Implement multi-agent workflows and orchestration
  • 3. Select agent architecture patterns
  • 4. Manage state, memory, and context
Topic 4: Implement text and speech analysis solutions10–15%- Implement speech capabilities
  • 1. Speech-to-text and text-to-speech integration
  • 2. Speech translation and speaker recognition
- Implement natural language processing
  • 1. Use Azure AI Language services
  • 2. Build conversational language understanding
  • 3. Perform sentiment analysis, entity recognition, and summarization
Topic 5: Plan and manage Azure AI solutions25–30%- Manage AI solution development lifecycle
  • 1. Configure model and agent deployments
  • 2. Integrate with CI/CD pipelines
  • 3. Monitor and maintain AI workloads
- Design Azure AI infrastructure
  • 1. Select appropriate Azure AI Foundry services
  • 2. Design for scalability, availability, and cost optimization
  • 3. Plan for security, compliance, and responsible AI

>> AI-103 Schulungsangebot <<

AI-103 Musterprüfungsfragen & AI-103 Exam Fragen

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Microsoft Developing AI Apps and Agents on Azure AI-103 Prüfungsfragen mit Lösungen (Q64-Q69):

64. Frage
You use Azure Custom Vision to build a classifier. After training is complete, which two metrics are available for review?

Antwort: B,E

Begründung:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Azure Custom Vision exposes precision and recall as primary metrics for evaluating trained model iterations.
Precision measures how many positive predictions are correct:
Precision = TP / (TP + FP)
A high precision value means that when the model predicts a class, it generates relatively few false positives.
Recall measures how many actual positive examples the model successfully identifies:
Recall = TP / (TP + FN)
A high recall value means that relatively few relevant examples are missed.
These metrics must be considered together. Increasing a prediction threshold can make the classifier more conservative, potentially improving precision while decreasing recall. Lowering the threshold often increases recall at the cost of additional false positives.
F-score can be calculated from precision and recall in general machine-learning practice, but it is not one of the two Custom Vision review metrics requested by the choices. Area under the curve and weighted accuracy likewise are not the standard selections represented in this question.
The AI-103 Study Guide requires understanding of computer-vision model implementation and operational evaluation when creating object/component-identification solutions. Microsoft Learn


65. Frage
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure image moderation to block unsafe content before processing the images.
Does this meet the goal?

Antwort: A

Begründung:
Correct:
* You configure a prompt shield for documents.
Prompt Shield for Documents: Highly Effective (Critical Defense)
How it helps: This shield specifically scans untrusted, third-party data inputs (like external documents or text extracted from uploaded images).
Mechanism: It evaluates the extracted image text before it is sent to the LLM to identify hidden jail
* You configure a prompt shield for user prompts.
Prompt Shield for User Prompts: Partially Effective (Defense in Depth)
How it helps: This shield targets direct jailbreak attempts written manually by the user in the text prompt field accompanying the upload.
Mechanism: It prevents the user from typing supporting instructions that prime the model to execute the hidden instructions found within the image.
* You configure image moderation to block unsafe content before processing the images.
Implementing rigorous image moderation is one of the most effective ways to secure multimodal AI systems against these threats. Moderation acts as a necessary gatekeeper, preventing malicious inputs from ever reaching the generative model.
Incorrect:
* You configure protected material detection.
Protected Material Detection: Ineffective for this Threat
Why it does not help: This feature is designed to scan model outputs to prevent the generation of copyrighted text, proprietary source code, or licensed imagery.
Limitation: It does not scan inputs for adversarial instructions and will not prevent a user from manipulating the model's logic.
Reference:
https://www.upgrad.com/blog/what-is-multimodal-ai/
https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/jailbreak-detection


66. Frage
A legal team must extract clauses and inferred fields from unstructured contracts. One required field is the contract end date, which is not stated explicitly and must be derived from the start date plus the term. The team has no labelled training data. Which tool best fits?

Antwort: B


67. Frage
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?

Antwort: D

Begründung:
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.


68. Frage
You are building an image sharing app that will use Azure AI to prevent users from sharing sexually explicit images.
You need to ensure that inappropriate images are identified correctly. The solution must minimize development effort.
What should you use?

Antwort: A

Begründung:
To prevent users from sharing sexually explicit images, you need a tool that can handle content moderation specifically designed for inappropriate content such as adult or sexually explicit material. Azure AI Content Safety Studio is a service designed for this purpose, providing pre- built AI models that can detect inappropriate content like adult images, violence, and other sensitive material. It minimizes development effort by offering an easy-to-use, pre-configured service for content safety without the need to build custom models.


69. Frage
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