AI-102 Azure AI Engineer Associate Dumps & Practice Exams 2026

Master the AI-102 Azure AI Engineer Associate exam with our curated dumps and realistic practice questions tailored for 2025. These exam materials focus on real-world scenarios across natural language processing, computer vision, conversational AI, and Azure cognitive services. Whether you're preparing for your first attempt or looking to close last-minute knowledge gaps, these AI-102 dumps and practice tests provide exam-style questions and detailed explanations aligned with the latest Microsoft objectives. Boost your readiness and confidence with hands-on preparation that mirrors the real certification experience.

AI-102 Azure AI Engineer Associate Dumps & Sample Questions List :

1. You need to select an Azure service for a solution that will generate natural‑language responses using a foundation model and also support image generation for marketing content. Which two services from Azure AI Foundry should you provision? (Choose two)

1. Azure AI Vision

2. Azure OpenAI in Foundry Models

3. Azure AI Search

4. Azure AI Document Intelligence

5. DALL‑E in Azure OpenAI

2. A retailer wants to detect customer movements in a store to improve layout design. Which service should you use?

1. Azure AI Vision Spatial Analysis

2. Azure AI Speech

3. Azure AI Search vector store

4. Azure AI Document Intelligence

3. When planning a generative AI solution using Azure OpenAI, which actions support Responsible AI and content moderation? (Choose all that apply)

1. Configure content safety filters and blocklists

2. Optimize and manage resource scalability

3. Implement prompt shields and harm detection

4. Use DALL‑E to generate images only at test time

4. You need to extract key phrases, entities, sentiment, and PII from a set of documents. Which Azure AI service should you use?

1. Azure AI Vision

2. Azure AI Language

3. Azure AI Search

4. Azure AI Speech

5. You want to build a computer vision solution that analyzes images for tags, objects, and text. According to the exam guidelines, what steps should you perform to create and deploy the appropriate Azure AI service? (Select one)

1. Provision an Azure AI Vision resource, specify visual features in the API request, and interpret the response

2. Provision an Azure AI Document Intelligence resource, train a custom model, and evaluate model metrics.

3. Provision an Azure AI Search resource, create an index, and perform ranking.

4. Provision an Azure AI Speech resource, configure SSML, and deploy a custom voice model.

6. You are building an application that transcribes speech to text and translates it to another language. According to the exam guidelines, which service should you use?

1. Azure AI Speech with translation features

2. Azure AI Document Intelligence

3. Azure AI Video Indexer

4. Azure AI Vision

7. Which of the following are recommended practices when managing and securing an Azure AI Foundry service? (Choose two)

1. Monitor the Azure AI resource and manage costs

2. Regenerate service keys regularly and store them in Azure Key Vault

3. Expose the API key in client‑side JavaScript for convenience

4. Use role‑based access control (RBAC) and Azure Active Directory for authentication

8. When building a custom speech recognition model using Azure AI Speech, what steps are involved? (Choose two)

1. Integrate generative AI speaking capabilities

2. Implement text‑to‑speech and speech‑to‑text functionality

3. Improve text‑to‑speech using SSML

4. Detect and classify documents

9. You are using Azure AI Foundry to implement a prompt flow for a generative AI solution. Which tasks are part of this process? (Choose two)

1. Deploy a hub, project, and necessary resources for Foundry

2. Implement a retrieval‑augmented generation (RAG) pattern by grounding the model in your data

3. Use SSML to improve speech synthesis

4. Train and publish a knowledge base for a question‑answering model

10. To create a custom language understanding (LUIS replacement) model in Azure AI Language, which actions should you perform? (Select three)

1. Define intents, entities, and utterances

2. Train, evaluate, deploy, and test the model

3. Export a knowledge base

4. Add alternate phrasing and chit‑chat to the model

5. Create data sources and indexers

11. What configuration options can be used to control the behavior of an Azure OpenAI model?

1. Adjust temperature and top‑p sampling parameters

2. Use vector index queries to limit results

3. Modify the cognitive services endpoint region

4. Enable DALL‑E image generation

12. You have a collection of FAQs and want to build a solution that provides short answers to user questions in a chat interface. Which steps are necessary? (Select two)

1. Create a custom question answering project

2. Add question‑answer pairs and import sources

3. Deploy a DALL‑E model

4. Use SSML to improve speech synthesis

13. You are deploying a generative AI model to run on edge devices where connectivity is limited. Which deployment option is appropriate?

1. Deploy the model as a standard Azure AI Foundry resource in the cloud

2. Deploy a container for local and edge use

3. Deploy the model using Azure AI Vision

4. Use Azure AI Search with vector search capabilities

14. To enable a multi‑turn conversation in Azure AI Language question answering, you should:

1. Train and publish a custom vision model

2. Create a multi‑turn conversation by defining follow‑up prompts and context

3. Use Semantic Kernel to build an agent

4. Deploy the model as an edge container

15. A customer asks you to prevent harmful behavior such as jailbreak prompts in a chat solution. Which Azure AI features should you implement? (Select one)

1. Prompt shields and harm detection

2. Custom vision model evaluation

3. Language understanding intents

4. Custom document processing

16. When implementing an Azure AI Search solution, which actions are required? (Choose two)

1. Provision a Search resource and define an index and skillset

2. Create data sources and indexers

3. Train a custom language understanding model

4. Implement speech‑to‑text translation

17. You are developing a virtual assistant using Azure OpenAI. Which tasks should you perform to configure an assistant? (Choose two)

1. Provision an Azure OpenAI in Foundry Models resource

2. Select and deploy an appropriate model

3. Implement a custom voice model using SSML

4. Create a multi‑turn knowledge base conversation

18. In Azure AI Search, you can enable semantic search and vector store features for which purposes? (Choose two)

1. Improve relevance by using semantic ranking

2. Store high‑dimensional embeddings for similarity search

3. Generate images from text prompts

4. Train a custom object detection model

19. Which statements about Azure AI agents are true? (Choose two)

1. You need to provision resources to build an agent

2. Agents are limited to single‑user workflows and cannot orchestrate multiple agents

3. Complex workflows can be implemented using Semantic Kernel or AutoGen

4. Agents are only available for computer vision solutions

20. You need to extract structured data from invoices and receipts. Which Azure AI service and approach should you use?

1. Azure AI Vision with image analysis

2. Azure AI Search with semantic search

3. Azure AI Document Intelligence using prebuilt models

4. Azure AI Speech with text‑to‑speech

21. In a custom vision project, when should you choose an image classification model instead of an object detection model?

1. When you need to identify and locate multiple objects in an image

2. When you only need to categorize the entire image into a single label

3. When you want to extract handwritten text from an image

4. When you need to publish a model to a container

22. Which steps are needed to build and use a custom document intelligence model? (Choose three)

1. Provision a Document Intelligence resource

2. Train, test, and publish the model

3. Compose multiple sub-models into a single model

4. Publish the model as a semantic search index

5. Use SSML to improve speech clarity

23. Which steps are necessary to create and deploy a custom image classification model with Azure AI Vision? (Choose three)

1. Label the images

2. Train the model and evaluate metrics

3. Publish the model

4. Consume the model using Azure AI Search

5. Use Semantic Kernel to implement complex workflows

24. For an end‑to‑end solution that extracts text from scanned documents and then summarizes and classifies them, which Azure AI services are most appropriate?

1. Azure AI Vision OCR to extract text

2. Azure AI Content Understanding to summarize and classify documents

3. Azure AI Search to generate images

4. Azure AI Speech to extract sentiments

25. Which statement best describes integrating Azure AI Foundry services into a continuous integration/continuous deployment (CI/CD) pipeline?

1. It’s unsupported because Azure AI services cannot be automated.

2. You should use infrastructure‑as‑code templates (like ARM or Bicep) to provision resources and deploy models

3. Only generative AI models can be included in a pipeline; computer vision models cannot.

4. CI/CD integration is limited to Azure AI Search only.

26. As an AI engineer, how can you manage costs for Azure AI services?

1. Monitor usage metrics and set spending alerts

2. Expose service keys publicly to encourage usage

3. Disable logging and monitoring to reduce overhead

4. Use AI services only in free tier environments

27. Which statement differentiates knowledge mining from information extraction in Azure AI services?

1. Knowledge mining focuses on indexing and searching content with semantic and vector search, while information extraction focuses on extracting structured data from documents

2. Both are identical concepts

3. Information extraction is only available through cognitive search

4. Knowledge mining cannot work with video or audio content

28. Which element is part of designing a responsible AI governance framework?

1. Creating a multi‑language question answering solution

2. Implementing content filters and blocklists

3. Deploying a custom image classification model

4. Implementing vector search for semantic relevance

29. When monitoring an Azure AI resource, which metrics or logs should you consider collecting? (Choose two)

1. Request latency and throughput

2. Number of images generated

3. Key rotation frequency

4. Cost metrics and usage quotas

30. When fine‑tuning a generative model with Azure OpenAI, which activities should you perform? (Choose two)

1. Collect a domain‑specific dataset and upload it to Azure storage

2. Use the Azure OpenAI API to create and deploy a fine‑tuned model

3. Use SSML markup to improve voice quality

4. Regenerate model keys to reset usage quotas

31. To integrate Azure OpenAI into your application via SDK, what steps must be taken? (Choose two)

1. Retrieve the endpoint and API key from the Azure portal

2. Install the appropriate SDK and authenticate requests

3. Register the application in Azure DevOps

4. Use the Document Intelligence SDK for all AI services

32. Which prompt engineering techniques can improve responses from a generative model? (Choose two)

1. Providing clear context and examples

2. Embedding malicious prompts to test moderation

3. Including user instructions and desired format

4. Disabling temperature sampling

33. Which actions enable a multi‑language question answering solution using Azure AI Language? (Choose two)

1. Export the knowledge base to a translation service and import the translations

2. Train separate language models for each language

3. Use the multi‑language feature of Azure AI Language question answering

4. Configure an Azure AI Speech translation pipeline

34. What insights can be extracted using Azure AI Video Indexer? (Choose three)

1. Face detection and speaker indexing

2. Sentiment analysis in text transcripts

3. Key frame extraction and object labels

4. Vector embeddings for similarity search

35. When customizing translation models with Azure AI Translator, which steps are involved?

1. Train, improve, and publish a custom translation model

2. Create a skillset and knowledge store projection

3. Use Semantic Kernel to orchestrate translation agents

4. Deploy the model as a container

36. In Azure AI Search, what are knowledge store projections, and why are they used?

1. Projections convert speech into text for indexing

2. Projections store enriched content in formats such as files, objects, or tables

3. Projections generate images from search results

4. Projections provide client‑side encryption keys

37. For a deployed generative model in Azure AI Foundry, which monitoring activities are necessary? (Choose two)

1. Monitor performance and resource consumption

2. Disable telemetry to reduce costs

3. Configure tracing and collect feedback

4. Only monitor after the first year of operation

38. Why is it necessary to create data sources and indexers in Azure AI Search?

1. To fine‑tune a generative model

2. To ingest and enrich content from external systems into the search index

3. To train a custom vision model

4. To generate synthetic training data

39. After developing an Azure AI agent, how should you test and optimize it before deployment?

1. Deploy the agent directly into production

2. Use the agent testing tools in Azure AI Foundry to evaluate performance, then adjust prompts and workflows as needed

3. Replace Semantic Kernel with custom C# code

4. Train a custom vision model

40. Semantic Kernel and Autogen are used in Azure AI agents for what purpose?

1. Building image classification models

2. Implementing complex workflows, including orchestration of multiple agents and users

3. Deploying speech translation pipelines

4. Creating prebuilt document extraction models

41. When planning an AI solution that meets Responsible AI principles, which considerations are important? (Choose two)

1. Selecting models appropriate to your use case

2. Ignoring bias and fairness issues because the model comes from Microsoft

3. Implementing content safety measures and filters

4. Disabling logging to protect privacy

42. Which Azure AI service can help you implement real‑time content moderation for text and images?

1. Azure AI Document Intelligence

2. Azure AI Content Safety (within Responsible AI features)

3. Azure AI Search

4. Azure AI Speech

43. Which practices facilitate integrating AI models into your application code? (Select two)

1. Use SDKs and REST APIs to call AI services

2. Hard‑code secret keys into source code for easier access

3. Implement client libraries that abstract calls to AI services

4. Disable authentication for quicker testing

44. You want to orchestrate multiple generative models within a single solution. According to the guidelines, what should you do?

1. Use Azure AI Search to index multiple models

2. Enable tracing and collect feedback to optimize the orchestration

3. Create an OCR pipeline

4. Train a custom vision model

45. How can Speech Synthesis Markup Language (SSML) be used in Azure AI Speech?

1. To extract handwritten text from documents

2. To fine‑tune generative models

3. To improve text‑to‑speech by controlling pronunciation and pitch

4. To generate images from text

46. Which description best explains the retrieval‑augmented generation (RAG) pattern?

1. Fine‑tuning a model on domain‑specific data to reduce hallucinations

2. Grounding a generative model on external data sources through search and vector retrieval

3. Generating synthetic images to augment training data

4. Translating speech to multiple languages

47. What steps are involved in building and deploying a custom knowledge base using Azure AI Language? (Choose two)

1. Add question‑answer pairs and import sources

2. Train, test, and publish the knowledge base

3. Deploy the base as a custom vision model

4. Use Azure AI Vision OCR to index the base

48. What is the difference between Azure AI Translator and Azure AI Speech?

1. Translator provides real‑time audio translation, while Speech only transcribes text

2. Translator is used for translating text and documents, whereas Speech provides speech‑to‑text, text‑to‑speech, and speech translation capabilities

3. Both services offer identical functionality

4. Speech can extract entities from text, while Translator cannot

49. When would you choose to train a custom translation model rather than use the standard Azure AI Translator model?

1. When you need general translation between widely used languages

2. When you require industry‑specific terminology or domain language support

3. When you want to detect PII in text

4. When you need to extract key phrases from documents

50. What capabilities does Azure AI Content Understanding provide? (Choose two)

1. Summarize, classify, and detect attributes of documents

2. Extract entities, tables, and images from documents

3. Implement multi‑agent workflows

4. Generate natural language responses

FAQs

1. What is the Microsoft Azure AI Engineer Associate AI-102 certification?

The AI-102 certification validates your ability to design, build, and deploy AI solutions using Microsoft Azure Cognitive Services, Azure Machine Learning, and related AI tools.

2. How do I become an Azure AI Engineer Associate certified professional?

You must pass the AI-102: Designing and Implementing a Microsoft Azure AI Solution exam, which tests your knowledge of Azure AI services, machine learning, and natural language processing.

3. What are the prerequisites for the Microsoft AI-102 certification exam?

There are no strict prerequisites, but it’s recommended to have prior knowledge of Python, Azure AI services, and REST APIs. Experience with data science or AI models is also helpful.

4. How much does the Azure AI Engineer Associate AI-102 exam cost?

The exam typically costs $165 USD, though pricing varies by region and local currency.

5. How many questions are in the AI-102 exam, and what is the time limit?

The exam includes 40–60 multiple-choice questions, and you’ll have 120 minutes to complete it.

6. What topics are covered in the Microsoft AI-102 certification exam?

The key domains include planning and managing AI solutions, computer vision, NLP, conversational AI, and responsible AI practices.

7. How difficult is the Azure AI Engineer Associate AI-102 exam?

It’s considered a moderate-to-advanced level exam, requiring both theoretical understanding and hands-on experience with Azure AI tools.

8. How long does it take to prepare for the AI-102 certification?

Most candidates prepare in 6–10 weeks, depending on their background in AI development and Azure services.

9. What jobs can I get after earning the Azure AI Engineer Associate certification?

You can work as an AI Engineer, Machine Learning Engineer, Cognitive Solutions Developer, or Cloud AI Specialist.

10. What is the average salary of an Azure AI Engineer Associate certified professional?

Certified professionals earn between $105,000–$145,000 per year, depending on experience and job role.