Microsoft AI-103 : Developing AI Apps and Agents on Azure
- Exam Code: AI-103
- Exam Name: Developing AI Apps and Agents on Azure
- Updated: Sep 28, 2026
- Q & A: 159 Questions and Answers
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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 2: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 3: Implement agentic solutions | 20-25% | - Manage agent operations
|
| Topic 4: Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Topic 5: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
Hotspot Question
You need to create a new resource that will be used to perform sentiment analysis and optical character recognition (OCR). The solution must meet the following requirements:
- Use a single key and endpoint to access multiple services.
- Consolidate billing for future services that you might use.
- Support the use of Azure Vision in Foundry Tools in the future.
How should you complete the HTTP request to create the new resource? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: PUT
Need to create a Cognitive Services Multi-Service resource using the PUT method. This specific resource type provides a single endpoint and key for multiple AI services, consolidates billing, and supports Azure Vision.
Box 2: CognitiveServices
Using the CognitiveServices kind creates a multi-service resource. This fulfills all the requirements by providing a single key and endpoint for multiple services, consolidating billing, and enabling access to features like Azure AI Vision and Text Analytics under one roof.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/sdk-overview
Hotspot Question
You have a Microsoft Foundry project that contains a customer support application.
You create an evaluation named Run1 that has the following configurations:
- Includes risk and safety metrics
- Includes the protected material evaluation
- Includes harmful content metrics that use a medium severity threshold You create an evaluation named Run2 that has the following configurations:
- Includes risk and safety metrics
- Includes the protected material evaluation
- Includes harmful content metrics that use a high severity threshold
You run both evaluations against a dataset named DB1 and receive the following results:
- Content harm defect rate of Run1: 12%
- Content harm defect rate of Run2: 4%
- Protected material evaluation of Run1: 6%
- Protected material evaluation of Run1: 6%
You start a fine-tuning job by using DB1. The job fails during automatic RAI checks for multiple content harm types.
You discover that the content filtering configuration is set to high severity.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: No
Changing the content filtering configuration to low severity will not resolve the fine-tuning job issues.
Box 2: Yes
The difference between a 12% defect rate in Run1 and a 4% defect rate in Run2 is entirely consistent with the different severity thresholds configured for the two evaluations.
Box 3: Yes
The protected material evaluation metric is entirely unaffected by changes to the content harm severity threshold.
Reference:
https://learn.microsoft.com/en-us/python/api/azure-ai-evaluation/azure.ai.evaluation
You have a Microsoft Foundry project that contains a high-traffic agent.
After a recent update, operational costs increase significantly.
Monitoring confirms that the volume of user traffic to the agent remains unchanged.
You suspect that changes to the request or response characteristics are causing the increase.
You need to identify whether the additional costs are driven by the model input size, the model output size, or expanded tool usage.
Which observability capability should you use?
- A. latency
- B. evaluation metrics
- C. run success rate
- D. token usage
Correct Answer: D 🗳️
Explanation: Only visible for Free4Torrent members. You can sign-up / login (it's free).
You have a customer support agent built by using the Microsoft Foundry Agent Service. The agent calls an Azure OpenAI model deployment.
During load testing, calls intermittently fail and return an HTTP 429 rate limit exceeded error.
You need to handle throttling to reduce call failures and improve reliability under load. The solution must remain within the service and model limits.
What should you do?
- A. Implement a retry policy that uses exponential backoff and jitter.
- B. Reduce the number of registered tools.
- C. Create a new thread and retry the calls immediately.
- D. Spit uploaded content into smaller files.
Correct Answer: A 🗳️
Explanation: Only visible for Free4Torrent members. You can sign-up / login (it's free).
In Azure AI Studio, you use Completions playground with the GPT-35 Turbo model.
You have a prompt that contains the following code.
function F(n)
{
var f = [0, 1];
for (var i = 2; i < n; i++) f[i] = f[i-1] + f[i-2];
return f;
}
You need the model to create an explanation of the code. The solution must minimize costs.
What should you do?
- A. Set the temperature parameter to 1.
- B. Add // what does function F do? to the prompt.
- C. Add function F(explanation) to the prompt.
- D. Change the model to GPT-4-32k.
Correct Answer: B 🗳️
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