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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have an Azure subscription.
You plan to build an app that will use the Azure OpenAI DALL-E model.
You need to deploy the model.
What should you use?

A) Azure AI Studio and Azure Command-Line Interface (CLI)
B) the Azure SDK for JavaScript and Azure Machine Learning Studio
C) the Azure SDK for Python and PowerShell cmdlets
D) the Azure portal and Microsoft Graph API

2. You have a Microsoft Foundry project that uses Azure AI Search to ground an agent in internal documentation.
After a recent content update, users report that the agent's answers have become less accurate.
You need to identify whether the retrieved content is negatively influencing the model's generated responses.
Which observability signal should you review?

A) indexer status and failure history
B) latency breakdown traces
C) prediction drift metrics
D) groundedness evaluation metrics

3. 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) Change the model to GPT-4-32k.
B) Add function F(explanation) to the prompt.
C) Add // what does function F do? to the prompt.
D) Set the temperature parameter to 1.

4. You are creating an agent workflow in a Microsoft Foundry project to support natural voice interactions.
The agent must receive continuous audio input, convert the input into text for reasoning, and then return spoken responses to a user. The workflow must meet the following requirements:
- Support turn-taking dynamics, where the agent begins to generate the
speech output before the user finishes speaking.
- Operate with low latency to maintain conversational experience.
You need to enable both speech to text and text to speech in a real-time agent interaction.
What should you do?

A) Use speech translation to convert the audio into another language and return the translated text.
B) Use batch transcription to convert the audio input and return text responses from the agent.
C) Use real-time speech to text for incoming audio and text to speech for agent responses.
D) Use an embeddings model to encode the audio, and then decode the audio into text and speech.

5. 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) evaluation metrics
B) run success rate
C) latency
D) token usage

Solutions: