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Microsoft AI-103 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
| Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
You have a Microsoft Foundry project named Project1.
Project1 contains an application that processes PDF vendor invoices.
You need to configure Azure Document Intelligence in Foundry Tools to generate a Markdown output that preserves the sections and table structure of the PDFs. The solution must minimize development effort.
What should you do?
- A. Increase the confidence threshold.
- B. Set the output_content_format=ContentFormat. MARKDOWN value.
- C. Configure output=figures when you analyze the PDF.
- D. Configure content=markdown when you analyze the document.
Correct Answer: B 🗳️
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You need to recommend a solution to assess the responses generated by Agent1 when the agent uses the product information stored in storage1. The solution must meet the technical requirements.
- A. a custom guardrail
- B. a coherence evaluator
- C. model fine-tuning
- D. a Retrieval Augmented Generation (RAG) evaluator
Correct Answer: D 🗳️
Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).
You have an Azure Al Search resource named Search1 that is used by multiple apps hosted in Azure. You need to secure Search1. The solution must meet the following requirements:
* Prevent access to Search1 from the internet.
* Limit the access of each app to query specific indexes.
What should you do? To answer, select the appropriate options in the answer area. NOTE: Each correct answer is worth one point.
Correct Answer:
Explanation:
* To prevent access from the internet: Create a private endpoint
* To limit access to query specific indexes: Use Azure roles
Create an Azure Private Link private endpoint for Search1 and disable public network access. The private endpoint assigns Search1 a private IP address within an Azure virtual network, allowing the hosted applications to reach the search service over the Microsoft backbone instead of through its public endpoint.
Disabling public network access ensures that requests originating from the public internet are rejected. An IP firewall would still expose the public endpoint to approved public IP addresses and therefore would not provide complete internet isolation.
For index-level authorization, enable Microsoft Entra ID role-based access control and assign Azure roles to each application's managed identity or service principal. The Search Index Data Reader role permits query and retrieval operations without allowing index modification. Azure AI Search supports scoping Search Index Data Reader or Search Index Data Contributor permissions to an individual index; custom role definitions can also be used when more precise permissions are required.
Query-key authentication is unsuitable because API keys normally provide service-level access and cannot reliably isolate applications to designated indexes.
Study Guide alignment: secure Azure AI services by configuring private endpoints, network isolation, managed identities, Microsoft Entra authentication, and role-based access control.
Your company is piloting a customer support agent in a Microsoft Foundry project name Project1. Project1 is connected to an existing Application Insights resource, and the company ' s support team reviews runs in the Traces tab.
The Foundry Agent Service is configured to perform the following actions:
* Retrieve the Application Insights connection string by calling
project_client.telemetry.get_application_insights_connection_string().
* Call configure_azure_monitor(connection_string=...) to enable telemetry.
A separate LangChain service configured to use OpenTelemetry and has the following configurations:
* Uses AzureAIOpenTelemetryTracer(connection_string=..., enable_content_recording=False)
* Passes the tracer by using config={ " callbacks " :[azure_tracer]}
Company policy has the following requirements:
* Telemetry from LangChain and OpenTelemetry must be distinguishable within the same Application Insights resource.
* Secrets and credentials must NOT be stored in prompts, tool arguments, or span attributes.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The LangChain service will appear in Traces without configuring a tracer: No Setting different OTEL_SERVICE_NAME values separates the services in Application Insights: Yes When using enable_content_recording=False, prompts and tool data will be captured in the telemetry: No The first statement is No because a separate LangChain or LangGraph application must emit telemetry through the configured tracing integration. Microsoft's LangChain tracing guidance states that you configure AzureAIOpenTelemetryTracer, attach it to the runnable or agent through callbacks, and then inspect the emitted traces in Azure Monitor. The troubleshooting guidance also states that missing LangChain or LangGraph spans are caused by tracing callbacks not being attached to the run.
The second statement is Yes . In OpenTelemetry, OTEL_SERVICE_NAME maps to the service.name resource attribute. Azure Monitor Application Insights uses cloud role names to represent separate services, and Microsoft states that when multiple services emit to the same Application Insights resource, cloud role names must be set so services are represented properly.
The third statement is No . enable_content_recording=False is specifically used to redact message content and tool call arguments from traces. Microsoft also advises disabling content recording in production and not storing secrets, credentials, or tokens in prompts or tool arguments. Reference topics: Microsoft Foundry tracing, LangChain tracing, OpenTelemetry service naming, Application Insights, and secure telemetry configuration.
You need to improve classification accuracy by showing the model how correct classifications look, without retraining the model or storing knowledge permanently.
- A. zero-shot learning
- B. Retrieval Augmented Generation (RAG)
- C. few-shot learning
- D. chain of thought
Correct Answer: C 🗳️
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