200+ Free Azure AI-102 Practice Questions

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Question 1

Score: 0/0

Your team needs to extract printed text, handwritten text, tables, and key-value pairs from vendor invoices. Which service should you choose?

Azure AI Search

Document Intelligence in Foundry Tools

Azure AI Translator

Azure AI Content Safety

to track

2026 Statistics

Key Facts: Azure AI-102 Exam

700/1000

Passing Score

Microsoft

40-60 Q

Typical Questions

Microsoft

100 min

Exam Duration

Microsoft

$165

US Exam Fee

Microsoft

6 domains

Weighted Areas

Current study guide

2025-12-23

Current Outline

Microsoft update

AI-102 is Microsoft's associate-level Azure AI engineer exam. Expect roughly 40-60 questions in 100 minutes, a 700/1000 passing score, and current objectives updated on December 23, 2025 to emphasize Microsoft Foundry terminology, generative AI, agentic workflows, and information-extraction solutions.

Sample Azure AI-102 Practice Questions

Try these sample questions to test your Azure AI-102 exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 200+ question experience with AI tutoring.

1Your team needs to extract printed text, handwritten text, tables, and key-value pairs from vendor invoices. Which service should you choose?

A.Azure AI Search

B.Document Intelligence in Foundry Tools

C.Azure AI Translator

D.Azure AI Content Safety

Explanation: Document Intelligence in Foundry Tools is built for OCR plus structured extraction from forms and business documents such as invoices. Azure AI Search can index extracted content, but it is not the service that performs the document field extraction itself.

2You need hybrid retrieval with keyword, semantic, and vector search over millions of knowledge-base chunks for a grounded chat app. Which service is the best fit?

A.Azure AI Language

B.Azure AI Search

C.Foundry Agent Service

D.Azure AI Content Safety

Explanation: Azure AI Search is the Azure service designed for indexing content and serving keyword, semantic, and vector retrieval patterns. Foundry Agent Service can use search as a tool, but it does not replace a search index for large-scale retrieval.

3An app running in Azure App Service must call Azure AI services without storing keys in configuration. Which authentication approach should you use?

A.A system-assigned managed identity with Azure RBAC

B.An API key stored in the page's JavaScript

C.A shared access signature (SAS) token

D.A hardcoded service principal secret in the source code

Explanation: A managed identity lets the app obtain tokens from Microsoft Entra ID without storing secrets in code or configuration. You then grant the identity the appropriate access to the Azure AI resource by using RBAC.

4You must deploy Azure OpenAI for a regulated workload that requires European data residency and private connectivity. What should you plan first?

A.Use any region because requests are automatically pinned to the user's geography

B.Choose a supported European region for the model and configure private networking for the resource

C.Create the resource in a U.S. region and enforce residency in application code

D.Use only a public endpoint with IP filtering because private networking is not relevant

Explanation: Region choice and network topology are foundational deployment decisions because model availability and compliance capabilities vary by region. If residency and isolation matter, you need a supported region that meets those requirements and a private access design such as private endpoints.

5You want one code path that uses local developer credentials on workstations and managed identity after deployment to Azure. Which credential strategy fits best with Azure SDK client libraries?

A.DefaultAzureCredential

B.AzureKeyCredential only

C.A connection string committed to source control

D.Anonymous access

Explanation: DefaultAzureCredential tries several authentication methods in a standard order, which makes it useful across local and deployed environments. That reduces environment-specific code and supports a more secure path toward secretless production deployments.

6A production chat application has steady, high token throughput requirements and strict latency targets throughout the business day. Which Azure OpenAI deployment option is usually the best fit?

A.A standard deployment

B.A provisioned throughput deployment

C.A batch processing job

D.A fine-tuned deployment

Explanation: Provisioned throughput is intended for workloads that need reserved capacity and predictable performance characteristics. A fine-tuned deployment changes model behavior, but it does not by itself solve predictable throughput or latency requirements.

7Your internal prototype has unpredictable usage spikes and you want to pay for actual consumption instead of reserving capacity up front. Which Azure OpenAI deployment option should you start with?

A.A standard deployment

B.A provisioned throughput deployment

C.A disconnected container deployment

D.A fine-tuned deployment

Explanation: A standard deployment is usually the starting point for spiky or uncertain workloads because it aligns cost more closely with actual usage. Provisioned throughput is better when you need reserved capacity and consistent sustained throughput.

8Only private network clients should be able to reach your Azure AI resource endpoint. Which configuration best enforces that requirement?

A.Create a private endpoint and disable public network access

B.Enable CORS restrictions only

C.Regenerate the API key every day

D.Increase the service quota

Explanation: A private endpoint places traffic on your private network path, and disabling public network access closes the public endpoint. CORS and key rotation are useful for other concerns, but they do not by themselves enforce private-only connectivity.

9Your chat app must screen both user prompts and model responses for hate, sexual, violence, and self-harm categories. Which service is designed for that?

A.Azure AI Search

B.Azure AI Content Safety

C.Azure AI Translator

D.Document Intelligence in Foundry Tools

Explanation: Azure AI Content Safety is built to analyze text and other modalities for safety categories and severity levels. Azure AI Search and Document Intelligence solve different problems and are not moderation services.

10Before approving a model from Foundry Models for production, which artifact should you review to understand intended uses, limitations, and evaluation information?

A.The model card

B.The NSG flow log

C.The ARM deployment history

D.A budget alert

Explanation: A model card summarizes what a model is intended for, where it may perform poorly, and what evaluation evidence is available. That makes it a core Responsible AI input when deciding whether a model fits a real workload.

About the Azure AI-102 Exam

The AI-102 exam validates the hands-on skills needed to design, build, secure, and operationalize AI solutions on Azure using Microsoft Foundry, Azure OpenAI, Azure AI Search, Vision, Speech, Language, and Document Intelligence services.

Questions

50 scored questions

Time Limit

100 minutes

Passing Score

700/1000

Exam Fee

$165 USD (Microsoft / Pearson VUE)

Azure AI-102 Exam Content Outline

20-25%

Plan and Manage an Azure AI Solution

Select the right Microsoft Foundry services, deploy resources and models, integrate SDKs and APIs, secure resources, manage monitoring and costs, and implement responsible AI controls.

15-20%

Implement Generative AI Solutions

Build Foundry projects and prompt flows, deploy Azure OpenAI models, implement RAG, evaluate and optimize models, apply prompt engineering, and operationalize generative workloads.

5-10%

Implement an Agentic Solution

Create custom agents with Microsoft Foundry Agent Service and Microsoft Agent Framework, orchestrate multi-agent workflows, and test and optimize agent behavior.

10-15%

Implement Computer Vision Solutions

Analyze images and video, extract text with OCR, build custom vision models, and use spatial analysis and Azure AI Video Indexer capabilities.

15-20%

Implement Natural Language Processing Solutions

Analyze and translate text, implement speech scenarios, use SSML and custom speech, and build CLU and question answering solutions.

15-20%

Implement Knowledge Mining and Information Extraction Solutions

Build Azure AI Search indexes, skillsets, and vector search experiences, and implement Document Intelligence and Content Understanding pipelines.

How to Pass the Azure AI-102 Exam

What You Need to Know

Passing score: 700/1000
Exam length: 50 questions
Time limit: 100 minutes
Exam fee: $165 USD

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

Azure AI-102 Study Tips from Top Performers

1Build at least one end-to-end Foundry project that uses Azure OpenAI, Azure AI Search, and your own data so RAG, deployment, and evaluation feel routine.

2Practice the current terminology from the December 23, 2025 outline, especially Microsoft Foundry, Foundry Tools, agentic solutions, and Content Understanding.

3Do not study services in isolation. Compare when to use Azure AI Search, Document Intelligence, Language, Speech, Vision, and Azure OpenAI for the same business scenario.

4Spend time on responsible AI and safety controls, including content moderation, content filters, blocklists, prompt shields, and governance design.

5Get comfortable with operational details such as authentication, keys, managed identities, monitoring, diagnostic settings, model deployment choices, and container options.

6Practice scenario questions that force tradeoffs between semantic search, vector search, custom models, prompt flow design, orchestration, and cost management.

Frequently Asked Questions

What is the AI-102 exam?

AI-102 is the exam for the Microsoft Azure AI Engineer Associate credential. It measures whether you can plan, build, secure, and maintain Azure AI solutions across Microsoft Foundry, Azure OpenAI, vision, speech, language, search, and document intelligence workloads.

How many questions are on AI-102 and how long do you get?

Microsoft role-based certification exams typically deliver about 40-60 questions. For AI-102, plan around a 100-minute exam duration and a passing score of 700 out of 1000.

What changed for AI-102 in 2026?

The current Microsoft study guide was updated on December 23, 2025. The latest outline uses Microsoft Foundry and Foundry Tools terminology, keeps the generative AI focus, includes an explicit agentic-solution domain worth 5-10%, and updates Document Intelligence and Content Understanding wording. If you prepared from older AI-102 notes, refresh the terminology before test day.

How hard is AI-102?

AI-102 is a challenging associate-level exam because it expects practical implementation skill, not just service recognition. Candidates need to know when to choose each Azure AI capability, how to secure and monitor it, and how to troubleshoot real solution-design tradeoffs across search, OpenAI, vision, speech, and language services.

How long should I study for AI-102?

Most candidates should plan for about 80-140 hours of prep over roughly 6-10 weeks, depending on prior Azure and AI experience. Strong preparation includes hands-on labs with Foundry projects, Azure OpenAI, Azure AI Search, Document Intelligence, and speech and language services, not just reading the docs.

What experience should you have before taking AI-102?

Microsoft does not list a formal prerequisite exam, but it recommends real experience building Azure AI solutions and familiarity with Python or C#, REST APIs, SDK usage, security, deployment, monitoring, and responsible AI. AI-900 is a useful foundation, but AI-102 expects much more applied skill.