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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Implement agentic solutions | 20-25% | - Build AI agents
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
A team wants to deploy a model from the Foundry catalogue with the fastest possible setup, pay only for the tokens they consume, and avoid provisioning any virtual machine (VM) quota. Which deployment option fits?
- A. A local container running on a developer workstation
- B. Managed compute (managed endpoint) deployment
- C. Serverless API deployment, also called models as a service (MaaS)
- D. Self-hosted deployment on Azure Kubernetes Service
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You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools.
You fine-tune a baseline speech to text model for the en-us locale and publish the model.
The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid.
You need to set the project property to the correct ID.
To what should you set the project property?
- A. the custom speech endpoint URL
- B. the project ID
- C. the custom speech project ID
- D. the project URL
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A customer support agent must answer from an internal knowledge base that is updated weekly with new product documentation, and its answers must always reflect the latest content. Which approach should you implement?
- A. Apply Direct Preference Optimization (DPO) to the documents
- B. Raise the model temperature to improve recall of recent facts
- C. Fine-tune the base model on the new documents every week
- D. Implement retrieval-augmented generation (RAG) over an index of the documents
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Drag and Drop Question
You have a Microsoft Foundry project that contains an agent. The agent uses threads and file uploads and calls an Azure OpenAI model deployment.
During load testing, calls intermittently fall and return an HTTP 429 rate limit exceeded error.
Some user uploads fail and generate an HTTP 400 file size exceeded error.
You need to mitigate the errors and reduce call failures. The solution must remain within the service and model limits.
What should you do to resolve each error? To answer, drag the appropriate actions to the correct errors. Each action may be used once, more than once or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each comet selection is worth one point.
Explanation:
Box 1: Implement exponential backoff and jitter in the retry logic
To remedy intermittent HTTP 429 rate limit errors while staying within service limits, you must implement a retry policy that uses exponential backoff and jitter.
Because the agent uses threads and file uploads, load testing triggers short-window concurrency spikes (bursting). Azure OpenAI evaluates rate limits in small slices (like 1- to 10-second windows), meaning overlapping file processing or concurrent thread steps will trigger a 429 even if your total Tokens-Per-Minute (TPM) or Requests-Per-Minute (RPM) look safe overall.
Box 2: Split content into smaller files before uploading the files.
Instead of uploading a large document in a single standard files.create request, utilize the Chunked Uploads REST API path. This splits the file payload on your client application layer before streaming it to Azure.
Note: To resolve the HTTP 400 file size exceeded error in your Microsoft Foundry agent, you need to bypass the strict payload constraints of standard single-request uploads. In Azure OpenAI and Microsoft Foundry Agent architectures, a multipart standard upload has a rigid request body limit (typically 30 MB or 50 MB.
Reference:
https://learn.microsoft.com/en-us/answers/questions/2265002/getting-error-after-deployed-a-model-in-azure-ai-f
https://learn.microsoft.com/en-us/answers/questions/5521436/getting-400-error(request-body-too-large)-for-batc
You are deploying a support agent that enables users to upload photos.
You need to automatically classify uploaded images for harmful content. The solution must block content based on severity levels.
What should you do?
- A. Enable prompt shields.
- B. Use blocklists.
- C. Implement image moderation.
- D. Apply keyword scanning to optical character recognition (OCR) output by using Azure Vision in Foundry Tools.
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