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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Microsoft Azure AI Solution |
| Exam Number: | AI-102 |
| Certificate Validity Period: | No expiration; certification remains valid unless Microsoft deprecates it |
| Exam Price: | $165 USD |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate |
| Available Languages: | Chinese (Simplified), Japanese, Korean, English |
| Passing Score: | 700 (on a scale of 1-1000) |
| Exam Format: | Active screen questions, Case studies, Multiple-choice, Drag-and-drop |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 40-60 |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | In-person at Pearson VUE testing centers or online proctored exam |
| Pre Condition: | Recommended: Understanding of Azure services, REST APIs, C#, Python, or JavaScript. No mandatory prerequisites, but Azure Administrator or Developer background is beneficial. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-102/ |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Generative AI Solutions | 10-15% | - Use Azure OpenAI Service for generative AI - Implement prompt engineering techniques - Apply responsible AI practices - Integrate Azure OpenAI models into applications |
| Topic 2: Implement Decision Support Solutions | 10-15% | - Design and implement decision automation - Integrate Azure AI services into applications - Monitor and optimize decision support solutions |
| Topic 3: Plan and Manage an Azure AI Solution | 15-20% | - Plan and configure secure AI solutions - Implement governance and compliance for AI solutions - Select appropriate Azure AI services and technologies - Manage AI costs and resource allocation |
| Topic 4: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Implement semantic and vector search - Create custom skills for Azure AI Search - Implement Azure AI Search solutions - Design document processing pipelines |
| Topic 5: Implement Natural Language Processing Solutions | 20-25% | - Implement speech capabilities using Azure AI Speech - Create conversational AI solutions with Azure AI Bot Service - Build question answering solutions - Translate text and speech - Analyze text using Azure AI Language |
| Topic 6: Implement Computer Vision Solutions | 15-20% | - Analyze images using Azure AI Vision - Implement Azure AI Face service - Process video and generate insights - Implement object detection and image classification - Read and process documents using Azure AI Document Intelligence |
Three full versions of the 425 practice questions for the Microsoft Designing and Implementing a Microsoft Azure AI Solution exam, all in one moderately priced package: a printable, expert-prepared PDF with instant download; a Desktop Test Engine for Windows that simulates the real exam with two practice modes and works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. You also receive a free demo, 365 days of free updates, a 50% renewal discount afterward, and unlimited computer installations.
The passing score for the Microsoft Designing and Implementing a Microsoft Azure AI Solution exam is 700 (on a scale of 1-1000), and registration runs $165 USD. Retakes mean paying again, so it pays to check your readiness first — the Exams4sures engines show your level honestly before you book.
Microsoft recommends these training resources for the Microsoft Designing and Implementing a Microsoft Azure AI Solution exam:
Combine the official training with the 425 practice questions from Exams4sures — the questions reveal how well the training actually settled.
The Microsoft Designing and Implementing a Microsoft Azure AI Solution exam gives you 120 minutes to answer 40-60. Time management is part of the test — rehearse with the timed mode in the Exams4sures Desktop Test Engine until pacing becomes automatic.
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The AI-102 exam is an official Microsoft certification exam covering the Microsoft Designing and Implementing a Microsoft Azure AI Solution syllabus detailed above. It counts toward these credentials: Microsoft Certified: Azure AI Engineer Associate. As the certification grows more important in its field, the exam stays demanding — which is exactly why structured practice matters. Exams4sures prepares you with 425 practice questions in PDF, Desktop Test Engine, and Online Test Engine formats.
In-person at Pearson VUE testing centers or online proctored exam Sign up for the Microsoft Designing and Implementing a Microsoft Azure AI Solution exam through these official channels:
Booked? Your Exams4sures practice questions arrive by email within 1 minute, so the countdown to exam day can start immediately.
Recommended: Understanding of Azure services, REST APIs, C#, Python, or JavaScript. No mandatory prerequisites, but Azure Administrator or Developer background is beneficial. Since policies evolve, confirm the current requirements on the official Microsoft exam page before scheduling.
The official Microsoft Designing and Implementing a Microsoft Azure AI Solution outline defines 6 content domains. The three heaviest are Implement Natural Language Processing Solutions (20-25%), Plan and Manage an Azure AI Solution (15-20%), and Implement Generative AI Solutions (10-15%). The full list is in the topics section above, and the 425 practice questions at Exams4sures cover each domain.
You have an Azure subscription.
You are building a chatbot that will use an Azure OpenAI model.
You need to deploy the model.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:

Explanation:
You are building a chatbot that will use an Azure OpenAI model.
You need to deploy the model in your Azure subscription.
Key Steps to Deploy an Azure OpenAI Model for a Chatbot:
Apply for access to Azure OpenAI
Azure OpenAI is a gated service and requires approval before you can provision resources.
Provision an Azure OpenAI resource
After approval, you must provision an Azure OpenAI resource in your subscription.
Deploy the GPT model
Since the chatbot requires natural language understanding and conversation, you deploy a GPT model (e.g., GPT-4, GPT-3.5).
Actions That Are Not Required for this Question:
Provision Azure API Management # Not needed for basic deployment (used only for advanced API management scenarios).
Deploy the embeddings model # Used for search/RAG, not necessary for chatbot baseline.
Deploy the DALL-E model # Used for image generation, not chatbot text responses.
Correct Order:
Apply for access to Azure OpenAI.
Provision an Azure OpenAI resource.
Deploy the GPT model.
Verified Answer (Sequence):
Apply for access to Azure OpenAI
Provision an Azure OpenAI resource
Deploy the GPT model
Microsoft References:
Quickstart: Get started with Azure OpenAI
Deploying models in Azure OpenAI
You are building a Language Understanding solution.
You discover that many intents have similar utterances containing airport names or airport codes.
You need to minimize the number of utterances used to fram the model.
Which type of custom entity should you use?
Correct Answer: B 🗳️
Explanation: Only visible for Exams4sures members. You can sign-up / login (it's free).
You have an Azure subscription that contains an Azure Al Video Indexer account.
You need to add a custom brand and logo to the indexer and configure an exclusion for the custom brand.
How should you complete the REST API call? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:

Explanation:
Comprehensive Detailed Explanation
The scenario:
You are working with Azure AI Video Indexer.
You need to add a custom brand/logo and also configure an exclusion so that this brand is not indexed.
In Video Indexer's REST API for brands, the configuration JSON includes:
" enabled " : true/false # Determines whether the brand should be indexed.
Setting " enabled " : false means the brand is excluded.
The other properties in the dropdown ( state , tags , useBuiltin ) are not correct in this context for configuring exclusions.
Therefore, to exclude a custom brand: " enabled " : false
The answer:
Attribute: " enabled "
Value: false
Microsoft References
Video Indexer - Brands customization
Azure Video Indexer API - Brands
You have an Azure Al Speech service resource named Resource1.
You call Resource1 by running the following Python code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth point.
Correct Answer:

Explanation:
The code creates a SpeechConfig and an AudioOutputConfig with filename= " path/to/file.wav " , then constructs a SpeechSynthesizer and calls speak_text_async(input) .
Existing file behavior: AudioOutputConfig with a filename directs synthesis output to that file path; the documentation emphasizes that it specifies the output file and that the parent directory must already exist. It does not require a unique filename (i.e., it does not fail purely because a file exists). In practice, the SDK writes to the specified path. Therefore, the statement claiming failure if a file already exists is No.
Voice source: The filename in AudioOutputConfig is output, not input. To choose a voice, you set speech_config.speech_synthesis_voice_name or use SSML; the WAV file isn't "sampled" to create a voice.
Hence this statement is No. Microsoft Learn
File generation: Using AudioOutputConfig(filename=...) with SpeechSynthesizer and speak_text_async(...) writes synthesized audio to that file. Many samples show the same pattern (often with .get() to wait for completion). So the function's purpose is to generate an audio file from the input text-Yes.
Note: In production, call .get() (or await) on speak_text_async(...) so the program waits for synthesis to complete before exiting. Microsoft Learn References (Microsoft Azure AI Speech) How to synthesize speech from text (create AudioOutputConfig to write to a file; synthesize with speak_text_async ).
Python AudioConfig class (audio output can be a speaker, audio file (WAV), or stream). Microsoft Learn NET AudioConfig.FromWavFileOutput (specifies output file; parent directory must already exist).
Microsoft Learn
Azure Q & A example (Python: speak_text_async(...).get() usage). Microsoft Learn
You have an Azure subscription that contains an Azure App Service app named Appl.
You provision a multi-service Azure Cognitive Services resource named CSAccount1.
You need to configure App1 to access CSAccount1. The solution must minimize administrative effort.
What should you use to configure App1?
Correct Answer: D 🗳️
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