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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. 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) Reduce the number of registered tools.
B) Create a new thread and retry the calls immediately.
C) Implement a retry policy that uses exponential backoff and jitter.
D) Spit uploaded content into smaller files.

2. You have a Microsoft Foundry project that contains three agents as shown in the following table.

You need to orchestrate the agents to ensure that the customer requests meet the following requirements:
- Support a deterministic, step-based process that uses conditional
branching and shared state across the agents.
- Optionally trigger a ticket action based on the triage result.
The solution must minimize development effort.
What should you include in the solution?

A) threads and runs without a workflow
B) separate agent runs coordinated in the application code
C) a workflow
D) a multi-agent group chat session

3. Hotspot Question
You have a Microsoft Founcy project that contains a Retrieval Augmented Generation (RAG) solution.
You need to run a pre-production evaluation by using labeled CSV dataset that contains the query, context, response and ground truth. The evaluation must measure the following:
- Whether responses address the user query
- Whether responses are supported by the provided context
- Whether responses contain sensitive or proprietary information
Which AI quality evaluation metrics should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

4. You have a large collection of image files and PDF documents stored in an Azure Storage account. The documents contain tabular data.
You need to extract the tables into a structured format that can be imported into a database. The solution must minimize development effort. What should you use?

A) Azure Content Understanding in Foundry Tools
B) Azure AI Video Indexer
C) Azure Vision in Foundry Tools
D) Azure Document Intelligence in Foundry Tools

5. You are building a text-to-speech solution that uses Azure Speech in Foundry Tools to read instructions from the script in a text file.
You discover that the solution often pronounces technical terms incorrectly.
You need to prevent the incorrect pronunciations. The solution must minimize development effort.
What should you do?

A) From Azure OpenAI use the Whisper model.
B) Use Speech Synthesis Markup Language (SSML) to adjust the prosody of the voice.
C) From Speech Studio, train a custom neural voice
D) Use Speech Synthesis Markup Language (SSML) to specify phonemes.
E) Use Speech Synthesis Markup Language (SSML) to apply say as rules.

Solutions: