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Microsoft Designing and Implementing a Microsoft Azure AI Solution : AI-102 Exam

AI-102 Exam Questions
  • Exam Code: AI-102
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution
  • Updated: Oct 19, 2026
  • Q & A: 425 Questions and Answers
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About Microsoft AI-102 Exam Braindumps

Microsoft AI-102 exam is a certification that validates the skills and knowledge of professionals in designing and implementing AI solutions using Microsoft Azure. AI-102 exam is intended for developers, data scientists, and AI professionals who want to demonstrate their expertise in designing and developing AI solutions on the Azure platform. Candidates who pass AI-102 exam will earn the Microsoft Certified: Azure AI Engineer Associate certification.

To pass AI-102 exam, the candidate needs to have a deep understanding of AI and machine learning concepts, as well as knowledge of various Azure services and tools that are used for implementing AI solutions. The candidate should also be familiar with programming languages such as Python and R, as well as various machine learning frameworks such as TensorFlow and PyTorch.

Microsoft AI-102 certification exam is a challenging exam that requires a lot of preparation and hands-on experience with Azure services. Candidates who pass AI-102 exam demonstrate their skills in designing and implementing AI solutions using Azure services, which is highly valued in the IT industry. Designing and Implementing a Microsoft Azure AI Solution certification is an excellent way for professionals to demonstrate their expertise in AI and advance their career in this field.

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Microsoft AI-102 exam is suitable for individuals who have experience in developing AI solutions and are looking to showcase their expertise in designing and implementing AI solutions using Microsoft Azure services. AI-102 exam also provides an opportunity for professionals to enhance their skills in the field of AI and prepare for a career in AI development.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102

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Microsoft AI-102 Exam Syllabus Topics:

SectionObjectives
Plan and Manage an Azure AI Solution- Manage security, compliance, and responsible AI
- Select appropriate Azure AI services
- Plan solution requirements and architecture
Implement Knowledge Mining and Document Intelligence- Enrich and index data for search scenarios
- Build Azure AI Search solutions
- Implement document intelligence solutions
Implement Generative AI Solutions- Implement Azure OpenAI Service solutions
- Integrate prompt engineering techniques
- Build retrieval-augmented generation (RAG) solutions
Implement Natural Language Processing Solutions- Implement question answering and conversational AI
- Translate and interpret language using Azure AI services
- Analyze text using Azure AI Language
Implement Computer Vision Solutions- Implement custom vision models
- Extract information using OCR and form recognition
- Analyze images and videos using Azure AI Vision

Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions:

Question #1

You are building a flight booking bot by using the Microsoft Bot Framework SDK.
The bot will ask users for their departure date. The bot must repeat the question until a valid date is given, or the users cancel the transaction.
Which type of dialog should you use?

  • A. waterfall
  • B. adaptive
  • C. action
  • D. prompt
Reveal Solution  Discussion  0

Correct Answer: D  πŸ—³οΈ

Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).

Question #2

You need to implement a table projection to generate a physical expression of an Azure Cognitive Search index.
Which three properties should you specify in the skillset definition JSON configuration table node? Each correct answer presents part of the solution. (Choose three.) NOTE: Each correct selection is worth one point.

  • A. tableName
  • B. generatedKeyName
  • C. dataSource
  • D. source
  • E. dataSourceConnection
Reveal Solution  Discussion  0

Correct Answer: A,B,D  πŸ—³οΈ

Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).

Question #3

You have a Custom Vision service project that performs object detection. The project uses the General domain for classification and contains a trained model.
You need to export the model for use on a network that is disconnected from the internet.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list o
' actions to the answer area and arrange them in the correct order.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
Change Domains to General (compact).
Retain the model.
Export the model.
You have a Custom Vision project that performs object detection. The project currently uses the General domain. You need to export the trained model for offline use (on a disconnected network).
Steps required:
Change Domains to General (compact).
Export is only supported with a Compact domain.
The General (compact) domain is optimized for exporting to devices (e.g., ONNX, TensorFlow, CoreML, etc.).
If you stay in "General" domain, you cannot export.
Retain the model.
After switching domains, you must retrain the model in the new compact domain to generate updated weights.
Export the model.
Once retrained in a compact domain, you can export the model for offline deployment.
Export formats include ONNX, TensorFlow, CoreML, or Docker container, depending on use case.
Why not the others?
Create a new classification model # Not needed; you already have an object detection model.
Change the classification type # Incorrect; the problem is with the domain type, not classification type.
Correct Answer Order:
Change Domains to General (compact).
Retain the model.
Export the model.
Microsoft References
Export a model from Custom Vision
Domains in Custom Vision

Question #4

Select the answer that correctly completes the sentence.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
semi-structured data
The statement is:
" Varying fields for each entity in a JSON document is an example of ______. " Relational data # Stored in structured tables with fixed schema (e.g., SQL database). Not applicable here.
Structured data # Has a strict schema (columns and types are predefined). JSON allows variation, so not structured.
Semi-structured data # Data that does not require a fixed schema, but still uses markers (tags, keys) to separate elements. JSON and XML are classic examples. # Unstructured data # Data without schema or labeling (e.g., videos, images, free text). Not JSON.
Since JSON documents can have varying fields per entity (flexible schema), this is considered semi-structured data.
The answer: semi-structured data
Microsoft References
Structured, semi-structured, and unstructured data
JSON in Azure

Question #5

You are building an app that will answer customer calls about the status of an order. The app will query a database for the order details and provide the customers with a spoken response.
You need to identify which Azure Al service APIs to use. The solution must minimize development effort.
Which object should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

For a phone-style bot that listens to customers and replies with spoken output, you use two core Azure AI Speech SDK objects:
SpeechRecognizer performs speech-to-text (STT), turning the caller's audio into text that your app can use to query the orders database. This minimizes effort because it handles audio capture/streaming, language models, and returns recognized text events/results directly.
SpeechSynthesizer performs text-to-speech (TTS), converting the retrieved order status text into natural- sounding audio to play back to the customer. It supports neural voices and simple calls like SpeakTextAsync()
.
Other options are not appropriate here:
TranslationRecognizer is for real-time speech translation, not needed for simple STT.
VoiceProfileClient is for speaker verification/identification, not for recognition or synthesis of the conversation content.
Microsoft References
Azure AI Speech SDK - Speech to text (SpeechRecognizer) overview and quickstarts.
Azure AI Speech SDK - Text to speech (SpeechSynthesizer) overview and quickstarts.
Azure AI Speech SDK concepts - recognition vs. synthesis pipelines.

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