AI-103 Developing AI Apps and Agents on Azure Exam
The AI-103 Developing AI Apps and Agents on Azure Exam is designed for developers who build, deploy, and maintain Artificial Intelligence applications and intelligent agents using Microsoft Azure AI services. This certification validates the skills required to create AI-powered solutions utilizing Azure OpenAI Service, Azure AI Foundry, Azure AI Search, Azure AI Vision, Azure AI Language, Azure AI Speech, and generative AI technologies.
Candidates preparing for the AI-103 exam learn how to develop enterprise-grade AI applications, integrate large language models (LLMs), create AI agents, implement retrieval-augmented generation (RAG), secure AI workloads, and optimize Azure AI solutions for scalability and performance.
Topics Covered in AI-103 Developing AI Apps and Agents on Azure Exam
Develop Generative AI Applications on Azure
Build AI Agents Using Azure AI Foundry
Azure OpenAI Service Integration
Prompt Engineering Best Practices
Retrieval-Augmented Generation (RAG)
Azure AI Search Configuration
Vector Search and Embeddings
Azure AI Language Services
Azure AI Vision Services
Azure AI Speech Services
Large Language Models (LLMs)
Responsible AI Implementation
AI Content Filtering and Safety
Semantic Kernel Development
Azure SDK for AI Development
Azure AI Foundry Projects
AI Agent Orchestration
Knowledge Grounding Techniques
Fine-Tuning AI Models
Monitoring AI Applications
AI Security and Compliance
Deploying AI Solutions on Azure
AI Application Optimization
Azure Resource Management
AI Workload Troubleshooting
Why Earn the AI-103 Certification?
The AI-103 certification demonstrates expertise in building modern AI-powered applications using Microsoft Azure technologies. Organizations worldwide are adopting generative AI, intelligent agents, and cloud-based machine learning solutions, creating strong demand for certified Azure AI developers.
Benefits include:
Enhanced Azure AI development skills
Recognition as an Azure AI professional
Better career opportunities
Higher earning potential
Expertise in Generative AI technologies
Practical experience with Azure AI services
Knowledge of AI governance and security
Professionals pursuing AI-103 often work as:
AI Developer
Azure AI Engineer
Generative AI Developer
Machine Learning Engineer
Cloud Developer
AI Solutions Architect
Intelligent Agent Developer
AI-103 Exam Preparation Resources
Successful candidates typically prepare using:
Microsoft Learn modules
Azure AI documentation
Hands-on Azure Labs
Practice Tests
Sample Questions
Exam Simulators
Real-world Azure AI projects
Candidates frequently search for AI-103 exam questions, AI-103 practice tests, Azure AI certification study guides, AI-103 dumps, AI-103 exam preparation materials, and AI-103 training resources to improve their chances of passing the exam.
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AI-103 Developing AI Apps and Agents on Azure Exam preparation materials, practice tests, study guides, and training resources. CertKingdom provides updated exam questions and learning materials to help candidates prepare efficiently and confidently.
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Topic 1, Case Study Contoso, Ltd
Overview
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative Al and
agent-based solutions by using Microsoft Foundry.
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 Al engineering team named Agent1Dev Team to optimize and
maintain existing Al solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design,
implement, monitor, and secure Al applications.
Contoso also has a team named Agent1Test Team that is responsible for validating Al solutions
before the solution deployments.
Generative Environment:
Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
Project1
Questions and Answers PDF 2/82
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 Al 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 Statement:
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.
Requirement:
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 Al
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.
Safety 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 Information:
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.
Question: 1
You need to configure Agent1 to answer customer questions about only the Contoso products.
The solution must meet the business requirements.
What should you do?
A. Apply top-p sampling.
B. Modify the system message instructions.
C. Add few-shot examples.
D. Increase the value of the temperature parameter.
Answer: B
Explanation:
The correct answer is B. Modify the system message instructions. The case study states that Agent1
answers general questions about Contoso products and that the business requirement is for Agent1
to answer questions only about the products sold by Contoso. This is a behavioral boundary for the
agent, so it should be implemented in the highest-priority instructions that define the agent’s role,
allowed scope, and refusal behavior.
Microsoft Foundry guidance states that a system message is used to steer model behavior, define the
assistant’s role and boundaries, and add safety or quality constraints for the scenario. The system
message should instruct Agent1 to answer only when the question concerns Contoso products, use
the configured Contoso product documentation as grounding, and politely refuse or redirect
questions about non-Contoso products.
Top-p sampling and temperature control randomness, not business-domain scope. Increasing
temperature would make responses less deterministic. Few-shot examples can support desired
behavior, but examples alone are weaker than explicit system-level instructions for defining
operating boundaries. Reference topics: system message design, prompt engineering, agent
instructions, response constraints, and grounded generative AI behavior.
Question: 2
You need to configure Agent1 to answer customer questions about only the Contoso products. The solution must meet the business requirements.
What should you do?
A. Apply top-p sampling.
B. Modify the system message instructions.
C. Add few-shot examples.
D. Increase the value of the temperature parameter.
Answer: B
Explanation:
The correct answer is B. Modify the system message instructions. The case study states that Agent1
answers general questions about Contoso products and that a business requirement is for Agent1 to
answer questions only about products sold by Contoso. This requirement defines the agent’s allowed
domain and refusal boundary, so it must be expressed in the agent’s system-level instructions.
Microsoft Foundry guidance states that system messages steer Azure OpenAI chat model behavior
and are used to define the assistant’s role, boundaries, output format, and safety or quality constraints.
The system message should instruct Agent1 to answer only Contoso-product questions, use Contoso
product documentation when available, and decline questions about non-Contoso products. This
directly enforces the intended business scope at the highest instruction level. Few-shot examples can
reinforce desired behavior but are not the primary control for defining mandatory operating
boundaries. Top-p sampling and temperature are decoding controls; they influence randomness and
diversity, not whether the agent restricts answers to a specific product domain. Increasing
temperature would likely reduce consistency. Reference topics: Microsoft Foundry agent instructions,
system message design, prompt engineering, response boundaries, and grounded generative AI behavior.
Question: 3
HOTSPOT
You need to ensure that Agent1Dev Team can access Agent1. The solution must meet the security and compliance requirements.
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.
Answer:
Explanation:
credential = DefaultAzureCredential()
agent = project_client.agents.get(agent_name=myAgent)
The correct authentication option is DefaultAzureCredential() because the case study states that API
keys must not be used to access Foundry-deployed models and that Contoso developers must
authenticate to Microsoft Foundry resources by using Microsoft Entra authentication. It also states
that access to Project1 must be assigned to Agent1Dev Team by using the security group
SC_Agent1_Dev. Microsoft Foundry authentication guidance recommends Microsoft Entra ID for
production workloads because it supports least-privilege RBAC, per-principal auditing, and keyless
authentication. AzureKeyCredential() would violate the no-API-key requirement, and None would
not provide a valid credential.
The correct agent operation is get because the task is to access an existing agent named Agent1, not
create a new version or retrieve a specific published version. Microsoft Foundry SDK examples show
AIProjectClient created with DefaultAzureCredential() and then using project agent operations to
create, retrieve, or interact with agents by name. To meet the compliance requirement, the group
SC_Agent1_Dev must also be granted the appropriate project-scoped Foundry role, such as Foundry
User, for Project1. Reference topics: Microsoft Entra authentication, Foundry RBAC, AIProjectClient,
and project agent access.
Question: 4
You need to recommend an invoice review solution that resolves the issue reported by the finance department.
What should you include in the recommendation?
A. Azure Content Understanding in Foundry Tools
B. chat completions
C. Azure Document Intelligence in Foundry Tools
D. Image Analysis
Answer: A
Explanation:
The correct recommendation is Azure Content Understanding in Foundry Tools. The case study states
that Contoso’s finance department must manually review vendor invoices to verify that invoice
details match vendor contract terms, and that the invoices contain tables, logos, and varied layouts
that make consistent processing difficult. It also states that the planned solution must evaluate both
the visual layout and textual content of the invoices.
Azure Content Understanding is designed for this type of multimodal document-processing
workload. Microsoft describes Content Understanding as a Foundry Tool that processes unstructured
and multimodal content, including documents and images, and transforms it into structured output
for AI applications. It can use document analyzers to extract text, layout, tables, fields, and
relationships from diverse document types.
Chat completions alone would not reliably extract structured invoice fields from complex layouts.
Azure Document Intelligence can extract OCR, layout, and tables, but Content Understanding is the
better end-to-end Foundry capability for combining visual and textual understanding with structured
extraction for downstream verification. Image Analysis focuses on image-level visual features and is
insufficient for invoice field and table review. Reference topics: Content Understanding, document
analyzers, multimodal extraction, invoice processing, tables, layout, and structured JSON output.
Question: 5
You need to recommend a solution to support the planned changes and technical requirements for
Agent1 to use the product information stored in storage1.
What should you include in the recommendation?
A. Azure Al Search
B. Azure Translator in Foundry Tools
C. Azure Document Intelligence in Foundry Tools
D. Grounding with Bing Search
Answer: A
Explanation:
The correct recommendation is Azure AI Search. The case study states that the product detail sheets
are stored as PDFs in storage1, and that Agent1 must be enabled to retrieve and use detailed product
information from those sheets. It also specifies that the indexing pipeline must enable semantic and
vector search, and that Agent1 must answer natural language questions about product details by
using the product sheet information. Azure AI Search is the Azure service designed to ingest content
from sources such as Azure Blob Storage, create searchable indexes, and support keyword, semantic,
hybrid, and vector retrieval for Retrieval Augmented Generation (RAG) solutions.
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Most Asked FAQs About AI-103
1. What is the AI-103 certification?
AI-103 validates skills in developing AI applications and agents using Microsoft Azure.
2. Who should take AI-103?
Developers, AI engineers, cloud professionals, and software engineers working with Azure AI.
3. What Azure services are covered?
Azure OpenAI, Azure AI Search, Vision, Speech, Language, and Azure AI Foundry.
4. Is programming required?
Yes, basic programming experience is highly recommended.
5. What languages should I know?
Python and C# are commonly used.
6. Does AI-103 include Generative AI?
Yes, Generative AI is a major focus area.
7. What is RAG?
Retrieval-Augmented Generation combines search and AI models to provide grounded responses.
8. How long should I study?
Most candidates spend 4–8 weeks preparing.
9. Is Azure OpenAI tested?
Yes, Azure OpenAI Service is an important exam objective.
10. What is Azure AI Foundry?
A platform for building and managing AI applications and agents.
11. Are AI agents covered?
Yes, agent development and orchestration are key exam topics.
12. Is hands-on Azure experience necessary?
Practical experience is highly beneficial.
13. What is the passing score?
Microsoft typically requires a passing score of 700 out of 1000.
14. What jobs can AI-103 help me obtain?
AI Developer, Azure AI Engineer, Generative AI Developer, and Cloud AI Specialist roles.
15. Is AI-103 worth earning?
Yes, it validates in-demand Azure AI and Generative AI development skills sought by employers worldwide.