Microsoft AI-103 Questions & Answers - in .pdf
- Vendor: Microsoft
- Exam Code: AI-103
- Exam Name: Developing AI Apps and Agents on Azure
- Features:
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- Exact Microsoft AI-103 Exam Questions with Correct Answers, verified by Experts with years of Experience in IT Field.
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- Vendor: Microsoft
- Exam Code: AI-103
- Exam Name: Developing AI Apps and Agents on Azure
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Microsoft AI-103 Exam Overview:
| Certification Vendor: | Microsoft |
| Exam Name: | Developing AI Apps and Agents on Azure |
| Exam Number: | AI-103 |
| Exam Duration: | 100 minutes |
| Exam Format: | Multiple choice, Scenario-based, Case study, Performance-based items |
| Passing Score: | 700 / 1000 |
| Available Languages: | English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil) |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate (retiring June 30, 2026) |
| Exam Price: | $165 USD (varies by country/region) |
| Real Exam Qty: | 50–60 |
| Certificate Validity Period: | 1 year (renewable annually via free online assessment) |
| Recommended Training: | Azure AI Foundry Documentation Microsoft Learn: Develop AI Apps and Agents on Azure |
| Exam Registration: | Pearson VUE Registration Microsoft Certification Dashboard |
| Sample Questions: | Microsoft AI-103 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended experience: 6+ months building AI applications, proficiency in Python or C#, familiarity with Azure services and AI concepts |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-103 |
Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
1. You have an Azure subscription.
You plan to build an app that will use the Azure OpenAI DALL-E model.
You need to deploy the model.
What should you use?
A) Azure AI Studio and Azure Command-Line Interface (CLI)
B) the Azure SDK for JavaScript and Azure Machine Learning Studio
C) the Azure SDK for Python and PowerShell cmdlets
D) the Azure portal and Microsoft Graph API
2. A copilot must answer multi-part questions that depend on earlier turns in the conversation. You want the system to break each complex question into focused subqueries, run them in parallel, and semantically rerank the results before composing an answer. Which Azure AI Search capability provides this?
A) Increasing the top-k value on a single keyword query
B) A single vector query with a high similarity threshold
C) Classic keyword search with a scoring profile
D) Agentic retrieval
3. In Azure AI Studio, you use Completions playground with the GPT-35 Turbo model.
You have a prompt that contains the following code.
function F(n)
{
var f = [0, 1];
for (var i = 2; i < n; i++) f[i] = f[i-1] + f[i-2];
return f;
}
You need the model to create an explanation of the code. The solution must minimize costs.
What should you do?
A) Add function F(explanation) to the prompt.
B) Set the temperature parameter to 1.
C) Add // what does function F do? to the prompt.
D) Change the model to GPT-4-32k.
4. Case Study 1 - Contoso, Ltd
Overview
Company Information
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative AI and agent-based solutions by using Microsoft Foundry.
Existing Environment
Identity Environment
Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.
Contoso recently formed a new AI engineering team named Agent1Dev Team to optimize and maintain existing AI solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement. monitor, and secure AI applications.
Contoso also has a team named Agent1Test Team that is responsible for validating AI solutions before the solution deployments.
Generative Environment
Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
Project1
Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.
Agent1 has the following configurations:
- Agent1 uses a base model deployment.
- A safety evaluation pipeline is NOT enabled.
- Tool invocation approval workflows are NOT enabled.
- Conversation memory constraints are NOT configured.
Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.
Project1 is deployed to an Azure region located in the European Union (EU).
Agent1Dev Team will use Project1 to optimize and maintain Agent1.
Project2
Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.
Development of the solution is incomplete.
Data Environment
Contoso stores product-related information in Azure resources that support AI applications.
The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.
Problem Statements
Contoso identifies the following issues:
- Agent1 has only general knowledge of the Contoso products.
- A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.
- Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.
- The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.
Requirements
Planned Changes
Contoso plans to implement the following changes:
- Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.
- Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.
- Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
- Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.
- Complete the development of the video creation solution.
Technical Requirements
Contoso identifies the following technical requirements:
- The model deployment used by Agent1 must support scalable, high-throughput generative AI workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.
- The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
- Responses generated by using the product sheet information must be relevant, complete, and accurate.
- Agent1 must be able to use the product sheets to answer natural language questions about product details.
- The model version used by Agent1 must remain consistent to ensure stable responses.
- The data processed by the model must remain within the EU.
Security and Compliance Requirements
Contoso identifies the following security and compliance requirements:
- API keys must NOT be used to access Foundry-deployed models.
- Access to the Azure resources must follow the principle of least privilege.
- The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.
- Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.
- Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.
- Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.
- The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.
Business Requirements
Contoso identifies the following business requirements:
- Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.
- Agent1 must answer questions only about the products sold by Contoso.
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. What should you include in the recommendation?
A) a groundedness evaluator
B) a Retrieval Augmented Generation (RAG) evaluator
C) a custom guardrail
D) model fine-tuning
5. Hotspot Question
You have a Microsoft Foundry project that contains a deployed chat model.
You have a Python service that sends API requests to the model. The service is integrated with an automated validation system that compares generated outputs against approved response patterns.
Stakeholders report that small wording differences are causing validation mismatches.
You need to update the request parameters to improve output stability. The solution must maximize reasoning quality.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: Only visible for members |
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