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Updated: Sep 16, 2026
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Microsoft AI-103 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI Apps and Agents on Azure |
| Exam Number: | AI-103 |
| Exam Duration: | 100 minutes |
| Real Exam Qty: | 40-60 |
| Certificate Validity Period: | 1 year |
| Related Certifications: | Microsoft Certified: Azure AI Apps and Agents Developer Associate |
| Available Languages: | English |
| Passing Score: | 700 |
| Exam Format: | Multiple choice, Hands-on labs, Case study, Scenario-based |
| Exam Price: | $165 USD |
| Sample Questions: | Microsoft AI-103 Sample Questions |
| Exam Way: | Online proctored or test center |
| Pre Condition: | Experience with Python programming and familiarity with Azure AI services, generative AI, and Microsoft Foundry is recommended. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-103 |
Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 2: Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Topic 3: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 4: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Topic 5: Implement agentic solutions | 20-25% | - Manage agent operations
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
You are creating an image-editing workflow in a Microsoft Foundry project.
The workflow must meet the following requirements:
- Ensure that background objects can be removed by applying a mask-
based inpainting edit.
- Preserve the original lighting and style of the edited images.
- Use the built-in image editing controls, NOT a custom model.
You need to ensure that image edits apply exclusively inside the masked area.
How should you configure the workflow?
- A. Enable image_to_image mode and a high-strength value to regenerate the full image based on the prompt.
- B. Enable text_to_image mode and a prompt describing the desired background removal.
- C. Set generation mode to image_variation and provide the original image as a reference.
- D. Enable mask_inpainting and supply both the input image and a mask indicating which part of the image to modify.
Correct Answer: D 🗳️
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You are designing a content management system.
You need to ensure that the reading experience is optimized for users who have reduced comprehension and learning differences, such as dyslexia. The solution must minimize development effort.
Which Azure service should you include in the solution?
- A. Azure AI Immersive Reader
- B. Azure Translator in Foundry Tools
- C. Azure Language in Foundry Tools
- D. Azure Document Intelligence in Foundry Tools
Correct Answer: A 🗳️
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An application must translate a high volume of standard product descriptions into 20 languages quickly, consistently, and at the lowest cost. The text is straightforward, with no domain-specific nuance. Which approach best fits?
- A. Azure Translator in Foundry Tools
- B. Azure AI Document Intelligence
- C. A large language model translation flow with a custom system prompt
- D. A separately fine-tuned model for each of the 20 target languages
Correct Answer: A 🗳️
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Hotspot Question
You are building a model to detect objects in images.
The performance of the model based on training data is shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: 0
The percentage of false positives is 0%.
Because the model made zero incorrect positive predictions, the count and percentage of false positives must be exactly zero.
Box 2: 25
The value for the number of true positives divided by the total number of true positives and false negatives is 25% (or 0.25).
High Precision (100%): Every single object your model detects is correct; it generates zero false alarms.
Low Recall (25%): Your model misses 75% of the actual objects it was supposed to find.
'
Reference:
https://medium.com/grabngoinfo/how-to-evaluate-the-performance-of-a-binary-classification-model-6e7193dcbbf9
You have a Microsoft Foundry project that contains a customer support agent built on a deployed chat model.
The agent responses are validated by using an automated testing system that compares generated answers to stored expected outputs. Identical prompts must return consistent response to prevent automated test failures.
You need to reduce response variability, without modifying the prompt or reducing factual accuracy.
What should you do for the model?
- A. Increase the temperature parameter.
- B. Decrease the temperature parameter.
- C. Remove stop sequences from the requests.
- D. Increase the max_tokens parameter.
Correct Answer: B 🗳️
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