For the Developing AI Apps and Agents on Azure (AI-103) web-based practice exam no special software installation is required. because it is a browser-based Developing AI Apps and Agents on Azure (AI-103) practice test. The web-based Developing AI Apps and Agents on Azure (AI-103) practice exam works on all operating systems like Mac, Linux, iOS, Android, and Windows. In the same way, IE, Firefox, Opera and Safari, and all the major browsers support the web-based Microsoft AI-103 Practice Test.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 2: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 4: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 5: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
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NEW QUESTION # 105
You are building a chatbot.
You need to ensure that the chatbot can classify user input into separate categories. The categories must be dynamic and defined at the time of inference.
Which service should you use to classify the input?
Answer: A
Explanation:
To classify user input into separate categories that are dynamic and defined at the time of inference, Azure OpenAI text classification is the best option. OpenAI models such as GPT can dynamically classify text into categories based on context and user-defined instructions. Since the categories need to be flexible and determined at inference time, OpenAI's natural language understanding capabilities are well-suited for this task.
NEW QUESTION # 106
You are building a web app named App1 that generates responses by using a model deployed to a Microsoft Foundry project named Project1.
Before sending the prompts to the model, App1 must retrieve documents by using Azure AI Search.
You need to integrate Project1 and App1. The solution must meet the following requirements:
- Multiple client applications must use the same search configuration.
- A security policy must prevent key-based authentication.
- Administrative effort must be minimized.
What should you do?
Answer: D
Explanation:
To meet your security and architecture requirements, you must add the Azure AI Search instance as a Connection within your Azure AI Foundry project and configure Managed Identities for role- based access control (RBAC).
To securely unify your search configuration without API keys, add the Azure AI Search instance as a shared Connection in your Azure AI Foundry project, disable key authentication on the search service, and authorize your applications using Azure RBAC and Managed Identities.
Note:
*-> 1. Create a Project Connection
Connect Azure AI Search directly inside the Azure AI Foundry hub or project.
*-> Share the same search service configuration across all connected client applications automatically.
Centralize your search endpoint details to reduce administrative overhead.
2. Disable Key Authentication
3. Enable Managed Identities
4. Update the Web App Code
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/tutorials/copilot-sdk-create-resources
NEW QUESTION # 107
You have a Microsoft Foundry project that contains an agent used by the financial analysts at your company.
You need to optimize the agent workflow by providing additional data access and processing capabilities. The solution must meet the following requirements:
* Ensure that the agent can perform calculations during conversations
* Ensure that the agent can access up-to-date information from public websites.
* Ensure that the agent can retrieve information from documents uploaded directly to the agent.
What should you use for each requirement? To answer, drag the appropriate tools to the correct requirements.
Each tool 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 correct selection is worth one point.
Answer:
Explanation:
Explanation:
Access up-to-date information from public websites: Grounding with Bing Search Perform calculations during conversations: Code interpreter Retrieve information from documents uploaded directly to the agent: File search The correct tool for public, current web information is Grounding with Bing Search . Microsoft Foundry Agent Service identifies Grounding with Bing Search as the built-in tool that enables an agent to access and return information from the internet, which fits the requirement for up-to-date public website data. ( learn.
microsoft.com )
For calculations during conversations, use Code interpreter . Microsoft's Foundry guidance states that Code Interpreter enables an agent to run Python code in a sandboxed execution environment and solve data analysis and math tasks iteratively. This is the correct fit for financial analysts who need calculations, analysis, and potentially chart generation during the conversation.
For documents uploaded directly to the agent, use File search . Microsoft describes File Search as the tool that enables Foundry agents to search through documents, retrieve relevant information, and augment model responses with knowledge from uploaded files such as PDFs, Word documents, and proprietary content.
Computer use is for interacting with graphical applications, not calculation or document retrieval. Microsoft Fabric is for enterprise data and analytics integration, not direct uploaded document retrieval. Reference topics: Foundry Agent Service tools, Code Interpreter, File Search, and Grounding with Bing Search.
NEW QUESTION # 108
You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed fine items and has a table-based layout.
Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution.
You need to create a single analyzer that meets the following requirements:
- Extracts the invoice number, invoice date, vendor name, and total
amount across varying templates
- Returns confidence scores so that results with confidence below 0.80
can be routed for supervisor review
What should you use?
Answer: C
Explanation:
The best option in this scenario is a custom Azure Content Understanding in Foundry Tools analyzer that defines the required fields as the extracted fields and the returned confidence scores for routing.
Custom Field Targeting with Confidence Scores: Azure Content Understanding allows you to build a document analyzer with a user-defined schema. By defining your required fields (InvoiceNumber, InvoiceDate, VendorName, and TotalAmount), the service will handle extraction across varying layouts and return a dedicated field-level confidence score. You can natively evaluate these confidence metrics to implement your supervisor routing workflow.
RAG-Ready Output Structure: The service naturally returns highly structured JSON payloads.
This makes it perfectly optimized to be ingested directly as grounding data for a Retrieval- Augmented Generation (RAG) agent.
Incorrect:
[Not C]
The prebuilt-layout analyzer extracts raw structural elements such as blocks of text, hierarchy, selection marks, and complete tables. It does not automatically classify or cleanly isolate specific target entities (like vendor name or total amount) into dedicated schema properties, leaving you with heavy post-processing work to isolate the text.
[Not D]
The prebuilt-documentSearch analyzer is optimized for broad, layout-aware text extraction for indexing. Relying on Azure AI Search's search.score for supervisor routing is conceptually flawed; search.score represents a relevance score for a search query ranking rather than an accuracy metric for data extraction confidence.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/document/analyzer-improvement
NEW QUESTION # 109
You are developing prompts for a Micosoft Foundry project that classifies incoming support tickets by category.
You need to improve accuracy by showing the model how correct classifications look, without retaining the model or storing knowledge permanently.
Which prompt engineering approach should you use?
Answer: A
Explanation:
Few-shot prompting is the best approach for this project. This technique improves classification accuracy by including a few high-quality examples directly inside the prompt, giving the model a clear pattern to follow without modifying its weights or storing data permanently.
Zero retraining: Works entirely through in-context learning during the API call.
No permanent storage: The knowledge disappears as soon as the inference request is completed.
Immediate accuracy boost: Demonstrates formatting, nuances, and edge cases directly to the model.
Reference:
https://www.linkedin.com/pulse/top-interview-questions-answers-prompt-engineering-nitin-sharma-ka4fc
NEW QUESTION # 110
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