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| Section | Objectives |
|---|---|
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
長い間AI-103認定を希望しているかもしれませんが、Microsoft勉強する時間や良い方法がありません。勉強はつまらないと思っていたのかもしれません。 AI-103の学習教材があなたの心を変えます。当社の製品を使用すると、すぐに勉強の幸せを感じるでしょう。熱心なPassTest専門家のおかげで、AI-103試験に合格するためのすばらしい学習ツールが考案されました。最初にデモを試してみると、勉強をやめられないことがわかります。 AI-103学習教材を使用すると、自分自身に挑戦して、もっと知りたいと思うでしょう。
質問 # 119
You have a Microsoft Foundry agent that grounds responses from an Azure Search index that contains the following:
- Searchable text fields for product names and product codes
- A vector field that stores embeddings for product descriptions
You need to ensure that users can query the index by using the following:
- Exact product names or codes
- Natural language descriptions of the products
What should you configure?
正解：D
解説：
To meet your requirements, you need to configure a Hybrid Search with Semantic Ranking in Azure AI Search. This setup combines keyword matching for exact identifiers with vector search for natural language queries, delivering the most accurate grounding data to your Microsoft Foundry agent.
Reference:
https://www.tredence.com/blog/searchsmart-enhancing-rag-with-azure-ai-search-service
質問 # 120
You need to configure the model deployment for Agent1 to meet the technical requirements.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Deployment type: Standard
Version update policy: Once the current version expires
The correct deployment type is Standard . The case study specifies that Project1 is deployed in an EU Azure region and that model-processed data must remain within the EU. It also requires scalable, high-throughput generative AI workloads that dynamically handle variable customer support traffic without reserved throughput capacity. In Microsoft Foundry Models, Standard is a pay-per-token deployment type that processes data in a single Azure region, while Global Standard can process requests across regions and Global Provisioned uses reserved provisioned throughput. Microsoft's deployment-type guidance identifies Standard as single-region, pay-per-token, whereas Global Provisioned is cross-region with reserved capacity.
The correct version update policy is Once the current version expires . This keeps Agent1 on the selected model version during its supported lifecycle, which supports stable and consistent responses, but still preserves continuity by automatically moving to a supported replacement when the current version is retired.
Microsoft's model versioning guidance states that this policy updates only when the current model version expires, while upgrading when a new default is available changes the deployment sooner and opting out can cause the deployment to stop working after retirement. Reference topics: deployment types, regional data processing, model versioning, throughput capacity, and stable production deployments.
質問 # 121
You have a factory that contains a production line.
You need to develop a solution that will analyze a video feed to detect and track the movement of employees as they work on the production line.
Which service should you include in the solution?
正解：A
解説：
Azure Vision in Foundry Tools (Spatial Analysis) can be used to analyze a video feed to detect and track the movement of employees as they work on the production line. This technology processes live or recorded video to understand spatial relationships and human activity.
Key capabilities for this application include:
Person Tracking & Movement: Spatial Analysis models identify human presence by detecting a body bounding box and tracking it in real time, enabling monitoring of movement patterns throughout a facility.
Zone & Line Monitoring: You can configure the system to detect when a person enters or exits a designated zone (e.g., a specific workstation) or crosses a line.
Dwell Time Calculation: The system can measure how long an employee spends in a specific zone, which can be useful for identifying bottlenecks.
Safety and Compliance: It can monitor for safety infractions, such as workers entering prohibited areas or failing to wear protective equipment (PPE).
Efficiency Analysis: By integrating these insights into Palantir Foundry's ontology, manufacturers can track assembly processes, optimize workflows, and enhance productivity.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/whats-new
質問 # 122
You have an Azure subscription that contains an Azure OpenAI resource.
You need to build an app that will suggest product names from a given product description.
Which model should you use?
正解：C
解説：
To suggest product names from a given product description, the ideal model is GPT-4. GPT-4 is a powerful language model that can understand and generate human-like text based on given inputs, making it well-suited for tasks such as generating creative suggestions like product names from descriptions.
Whisper is used for speech-to-text conversion, not for generating text suggestions.
DALL-E is designed for generating images from text prompts, not for text generation tasks like naming products.
Embeddings are used for tasks involving similarity search and semantic understanding but not for generating new text like product names.
質問 # 123
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools.
You fine-tune a baseline speech to text model for the en-us locale and publish the model.
The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid.
You need to set the project property to the correct ID.
To what should you set the project property?
正解：C
解説：
The correct answer is D. the custom speech project ID . For custom speech fine-tuning, the Speech to text REST API uses a project property that must refer to the Custom Speech project, not the general Microsoft Foundry project. Microsoft's Custom Speech guidance states that when using the Speech to text REST API for custom speech, you must set the project property to the ID of your custom speech project. It also explicitly notes that the custom speech project ID is not the same as the Microsoft Foundry project ID.
This distinction explains the invalid project ID error. Supplying the Foundry project ID, project URL, or endpoint URL does not identify the Custom Speech project that owns the fine-tuned speech model. The custom speech endpoint URL is used when calling a deployed custom model endpoint for recognition, but it is not the value of the REST API project property. The project URL is also not accepted because the API expects the identifier value. Reference topics: Azure Speech in Foundry Tools, Custom Speech fine-tuning, Speech to text REST API, custom speech project ID, model publication, and endpoint configuration.
質問 # 124
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